Implementing an effective Health Information Exchange (HIE) system

by Rishi Khanna

Integrating HIE systems_Blanc Labs

There are many avenues for secure electronic data transfer available today. And yet, most Canadians’ medical records are still kept on paper and stored in various medical offices or at the patients’ residences. 

Providers typically exchange information through fax, mail, or by patients bringing their records to appointments. When data is exchanged digitally, it is done through email, which may not always be secure. Also, the information may be in a myriad of file formats that may not be compatible with the medical providers’ systems.  

Electronic health information exchange (HIE) can significantly improve the completeness of medical records and greatly impact the care and treatment of patients. Doctors and caregivers can review details such as current medication, history and tests wholistically during visits. HIE systems can also make information available on time for better decision-making.  

 As a medical provider that has adopted email patient information into their process flow, you might ask: What is the need for transitioning into an electronic health information exchange?  

 The answer is standardization. Once data is standardized, you can seamlessly integrate the patient’s health information into your Electronic Health Record (EHR).  

For example, suppose you have patients with diabetes in your EHR, and you receive lab results electronically and incorporate them into your system. In that case, you can easily generate a list of patients with high blood sugar and schedule follow-ups in advance. 

At Blanc Labs, we have spent the last five years helping Canadian and US healthcare providers with their healthcare interoperability strategies.  An HIE is the first step towards achieving interoperability goals. In this article, I have briefly described how HIE works and it’s key features. I have also attempted to provide an overview of the standard architectural components associated with designing HIE systems, laying them down as a reference architecture that can serve as a blueprint for your organization. 

What is an Electronic Health Information Exchange (HIE) system?

An Electronic Health Information Exchange (HIE) system enables the secure and interoperable exchange of health information between different healthcare organizations and providers. As mentioned earlier, the goal of an HIE is to facilitate the sharing of patient health records and relevant clinical data in a standardized electronic format that improves access for healthcare professionals to use this information for better patient care. 

Some of the key features of an HIE are:  

  • Interoperability: HIEs are designed to support the exchange of health information across various healthcare systems, electronic health record (EHR) platforms, and health IT applications. This ensures that data can be seamlessly shared and understood by different healthcare providers. 
  • Patient Data Exchange: HIEs enable the sharing of essential patient information, such as medical history, allergies, medications, lab results, imaging reports, and other clinical data. This helps healthcare providers have a comprehensive view of a patient’s health, even if they have received care from multiple sources. 
  • Data Security and Privacy: HIEs implement robust security measures to protect patient information and ensure compliance with privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA). Patient consent is often obtained before sharing their health information through the HIE. 
  • Coordination of Care: By facilitating the exchange of health information, HIEs support better care coordination among healthcare professionals, leading to improved patient outcomes and reduced duplication of tests and treatments. 
  • Public Health Reporting: HIEs can also be used to aggregate de-identified health data to monitor public health trends, track disease outbreaks, and support population health initiatives. 

What are some of the Electronic Health Information Exchange (HIE) systems available today?

Many healthcare information technology companies offer products with Health Information Exchange (HIE) capabilities. Some of the prominent ones include EPIC, Cerner, InterSystems, and Allscripts.  

Even though some of these products have similar HIE features, healthcare organizations have different needs when it comes to accessing patient data and sharing it in a standardized way with external parties. It is important to understand which HIE system works best for you so you can make informed decisions about your interoperability journey. 

HIE Architecture Components

The main objective of HIE architecture is to gather and store patient health information in a consistent way from various healthcare organizations. This enables authorized healthcare providers to access a complete and uninterrupted patient record. 

The key components of an HIE system are: 

  • Data Sources: The system should be able to integrate data from various sources, such as hospitals, clinics, laboratories, pharmacies, imaging centers, and other healthcare facilities. These sources generate patient data, including electronic health records (EHRs), lab results, radiology reports, medications, allergies, immunizations, and more. 
  • Interface Engines: Interface engines serve as middleware to facilitate data exchange between the various data sources and the HIE. They handle data transformation, validation, routing, and protocol conversion to ensure seamless integration of data from disparate systems. 
  • Data Repository: The heart of the architecture is the data repository, where all the integrated patient data is stored. This repository organizes data in a standardized and structured format to ensure consistency and ease of access. 
  • Master Patient Index (MPI): An MPI is used to maintain a unique identifier for each patient across the different data sources. This ensures that patient records are accurately linked, regardless of variations in identifiers or demographic data. 
  • Terminology Services: To ensure consistency and standardization of data, terminology services are used to map and manage different coding systems, such as SNOMED CT, LOINC, RxNorm, and others. 
  • Patient Portal and Provider Portal: HIEs often need to provide patient portals that allow patients to access their own health records and engage in their care. Provider portals enable authorized healthcare providers to view patient records and contribute to care coordination. 
  • Analytics and Reporting: To support population health management and quality improvement initiatives, HIEs may include analytics and reporting capabilities. This allows healthcare organizations to analyze health trends, identify gaps in care, and measure performance. 

Blanc Labs’ HIE Reference Architecture

In our experience, HIE systems should be viewed holistically and as the foundation of healthcare interoperability, preparing for integration and information exchange within the organization and with external partners as you mature along the interoperability journey. This means planning the data flow from end-to-end. 

This is the framework we recommend for building HIE systems: 

  1.  Standardization – This involves enabling the existing healthcare systems (LIS, RIS, HIS, EHR, EHR etc) to support a healthcare standard like HL7, CCDA, DICOM, FHIR etc. 
  2. Data Ingestion – This refers to establishing tools and infrastructure to ingest raw data for further processing. This generally involves leveraging established systems like interface engines. 
  3. Data Curation & Normalization – At this stage, the data coming from different systems gets normalized across different coding systems (e.g.  LOINC, SNOMED, etc.) as well as mapped to unique patients and physicians using local or national master patient and physician indexes. 
  4. FHIR access of data – Normalized data from the previous phase is now exposed using standard FHIR based API for internal/external applications. 
  5. Applications for FHIR – Various kinds of applications can now access a longitudinal view of patient data from a centralized location hence reducing errors and inefficiencies that will result in improved patient outcomes. 


Blanc Labs HIE Reference Architecture
Blanc Labs’ proprietary reference architecture for HIE

We have experience in integrating with various healthcare systems including EMRs like EPIC, Cerner, MEDITECH, AllScripts etc., and devices such as Apple Watch, blood sugar and blood pressure monitors, image scanning devices, among others.  

A Note about Security & Compliance

HIEs handle sensitive patient health information, so robust security measures, such as encryption, authentication, and authorization mechanisms, need to be implemented to protect patient privacy. Access controls should be enforced to ensure that only authorized healthcare providers can view and access patient information.  

Moreover, data governance policies should be defined to govern data quality, data sharing agreements, consent management, and compliance with relevant privacy regulations, such as HIPAA. 

Getting Started with HIE Systems

Blanc Labs is on a mission to improve end-to-end patient care by building healthcare interoperability solutions that enable accurate, timely and secure patient data access to patients and providers. Our teams utilize a strong and expandable approach to infrastructure that follows industry standards and regulatory requirements to guarantee the privacy and security of patient information. With our knowledge in data integration, data management, and interoperability standards, we enable healthcare providers to fully utilize their data, resulting in well-informed decision-making, personalized care delivery, and ultimately, improved patient outcomes. 

To learn more about how we can help with your interoperability journey, reach me here 

About Rishi Khanna:  

Rishi Khanna_Blanc Labs

VP of Engineering/ Healthcare Practice Lead 

Rishi Khanna is an entrepreneurial technology leader with extensive industry experience in architecting secure and scalable distributed systems using modern on-prem, cloud or hybrid infrastructure. Rishi’s role at Blanc Labs is to drive innovation by developing new offerings for the Healthcare domain, leveraging emerging technologies in the field of Software Engineering, Data, AI/ML, Cloud & DevOps. Rishi also leads Blanc Lab’s InfoSec practice that implements and maintains security compliance, which is essential for developing a high degree of trust with our clients. 


How to Embrace Innovation in Banking

In this period of economic change, while many banks are cutting spending on “discretionary” projects, it is important to remember that innovation is the linchpin that will help them survive and flourish in less favorable conditions.

The pandemic alerted traditional banks about the need to catch up with fintech platforms. In response, most banks have created powerful apps and websites to improve the user experience. Customers can now achieve most of their banking activities without a physical visit to the branch.

But is that enough to compete with modern fintech companies? And more importantly, are these new additions structurally impactful enough to bear the brunt of the next disruption?

“Smaller FinTech companies have rapid and low-cost access to these newer cloud-based technologies and are developing innovative, best-in-class solutions that address a number of the pain points that are costly for traditional banks to deal with and create friction in the customer’s digital experience.”

Source: PwC

Instead of facing challenges as they come, banks must stay ahead of the curve to stay competitive. In this article, we discuss the need for innovation and how you can ensure your bank stays at the forefront of innovation in banking.

The Need for Innovation in Banking


The need for innovation is driven by two key factors:

Customer Expectations are Becoming Customer Demands

The young generation of customers likes to stay informed about the latest tech. They are early adopters andincreasingly expectall businesses, especially banks, to offer them a convenient, highly integrated experience on whatever channel they engaging with.

Just as customers have grown accustomed to mobile banking, they are quickly becoming accustomed to receiving tailor made offers and incentives based on the payment card they are using or the retailers they frequent.   

Over time, these innovations will become table stakes. Traditional banks that fail to catch up will struggle for market share.

In December 2022, the Innovation in Retail Banking Report surveyed large national and regional banks (33%), credit unions (17%), and community & regional banks (24%) to identify trends in banking. As the chart above reveals, offerings including checking accounts, lending and investments, which can be foundational growth areas for banks, have seen little innovation since the pandemic (2020-2021).


Your competitors will make a run to implement the latest technologies because it’s an opportunity to rope in more customers. If you fail to keep up, your customers will take their business elsewhere.

Your competition might just be some of the largest banks in the world. It’s hard to compete unless you have the right team to support your innovation.

Citigroup hired 8,000 tech workers in 2023, and it already had 30,000 software engineers employed.

So, why is America’s fourth-largest bank going all-in on IT spending?

A GOBankingRates survey found that 85% of people prefer banking online with a mobile app. And Citi wanted to improve customers’ online banking experiences by investing heavily in a top-notch IT team.

Improving online experiences is only the tip of the iceberg. Many large banks and neobanks are already using technologies like AI and machine learning (ML) to develop innovative use cases for their customers.

Overcoming the Fear of Innovation

Employees maintain the status quo when leaders don’t encourage innovation and risk-taking. Leaders should appreciate innovative ideas to develop a culture that fosters innovation.

Here’s how you can help employees overcome the fear of innovation:

Embrace Creative Destruction

Banks are often reluctant to introduce products or services that can destroy an existing, profitable line of business.

This creates an opportunity for a competitor to enter the market with a new idea and disrupt it.

As Schumpeter’s Theory of Creative Destruction explains, capitalism evolves and new, innovative products continue to drive old products obsolete.

For example, companies like Apple and Samsung continue to introduce innovative products like foldable phones and flip phones. These products can impact the sales of existing products that sell well. But if they don’t introduce these products, their competitors will.

Instead of giving in to fear, consider extensive research and testing the product or service to minimize risk.

Take Calculated Risks

Build a culture that encourages calculated risk-taking. If you punish failures, why will anyone share more creative ideas with you?

“Management can state their encouragement for innovation, but it is the demonstration of acceptance for radical innovation that shows whether the support is truly there. People will only want to attempt something risky when there is little threat of significant negative consequences to their work or their career from a failure.”

Source: Journal of Strategic Leadership

Sure, risk-taking isn’t exactly encouraged in banking. But it’s necessary in the context of innovation.

Leaders must improve their response to failure. Instead of punishing failures, consider building a framework that helps creative team members navigate failures when innovating.

For example, encourage your team to identify barriers that led to failure. Help them create an action plan to improve in the next iteration. Provide them with the guidance and resources needed for research.

Encouraging calculated risks and providing a framework to navigate failure helps reduce the overall risk to the organization without stifling creativity or innovation.

Building a Culture of Innovation

Implementing new technologies successfully is a half-won battle. Sustaining the innovative spirit requires a culture that fosters innovation.

“Getting the technology implemented successfully is table stakes. Then you need to energize it with the right culture to drive innovation and transformation.”

Source: Accenture

Research by Jay Rao and Joseph Weintraub published in the MIT Sloan Management Review states that innovative culture requires six building blocks:

  • Values: A company’s values are reflected in leaders’ actions. Values that promote entrepreneurship, creativity, and continuous learning breed innovation.
  • Behaviors: The behavior of employees and leaders determines the success of innovation. An example of the behavior of leaders is how they act when it’s time to discontinue a product to make room for a new, innovative product. On the other hand, employees must proactively seek resolutions for technical difficulties and make the most of a limited budget.
  • Climate: Create a work environment where innovative ideas are rewarded with enthusiasm. Encourage people to take on challenges and risks and promote independent thinking to create an atmosphere conducive to innovation.
  • Resources: Innovation requires more than just funding. You need resources like technology and domain experts to experiment with or execute innovative ideas.
  • Processes: Banks need a well-defined process to bring an innovative concept to fruition. The process should include idea evaluation, the method for prioritizing ideas in the pipeline, and funding sources.
  • Success: Success is critical to funding future innovations. You can capture the success of innovation at three levels (external, enterprise, and personal). External success is especially important because it shows how well your innovation paid off and earns the company recognition as being innovative.

If you want to build a culture of innovation, make sure you have at least two to three of these building blocks solidly in place.

Enhancing the Customer Experience Through Innovation​

Customer experience is one of the most critical areas that innovation in banking can improve. Here are a few examples of how:

Omnichannel Banking

Omnichannel banking isn’t new. Your bank probably already allows customers to switch seamlessly between self-service, assisted, and full-service banking.

But there’s a major gap in terms of personalization between self-service and assisted banking.

Using AI to improve personal engagement can help digital and in-person banking channels work together.

It allows the banker to step in only when necessary, allowing the customer to self-serve otherwise.

For example, you can suggest debt investments to a customer who has been consistently investing in equities to rebalance their portfolio.

If your customer thinks rebalancing is a good idea, they’ll consider it or reach out for more advice.

Banking in the Metaverse

Currently, the best you can do is use AI to understand the customer’s behavior and push personalized suggestions.

However, according to Deloitte, bank branches remain the preferred method of banking for complex transactions like mortgages and wealth management across economies.

That’s a gap the metaverse can bridge.

Banking in the metaverse feels like a distant dream. But banks like J.P. Morgan are already investing in the metaverse.

The reason? The metaverse opens plenty of doors to provide personalized and immersive customer experiences.

Picture this: A customer wants to invest a substantial amount and needs advice.

Do you think he’ll want to make a decision over the phone or in just a single visit? Probably not.

The metaverse solves two problems here.

First, the customer can meet a virtual assistant or a bank representative’s avatar in the metaverse, just like in real life. Second, the customer can arrange multiple meetings without having to waste time physically traveling to the branch.

Open Banking

Open banking is a system that allows banks to access data from third-party financial services via open banking APIs (application programming interfaces).

Accessing third-party financial data offers plenty of opportunities for innovation. For example, you can partner with a fintech company to offer an innovative solution that you’ve created.

You can continue focusing on innovation while the fintech manages your innovative banking solutions.

Open banking allows you to provide BaaS (banking-as-a-service) to third-party financial services. You can allow them to connect to your database and charge a fee. The third party can use the data to offer more value to customers.

Also Read: How Blanc Labs and Flinks and overhauled mortgage application for EQ Bank

Regulatory and Security Considerations

Regulatory and security considerations are critical to ensuring you don’t end up in legal hot water or end up losing thousands of dollars (and your reputation) to scams.

Regulatory Considerations

Regulators want to encourage innovation while mitigating risks.

For example, central banks around the world have started to accept the idea of digital currencies. But so far, most cryptocurrencies have shown themselves to be poor stores of value that are often used for illicit purposes.

The key here is to work with the regulators to find common ground where digital currencies have a more stable value.

As McKinsey explains:

“Regulators also play a critical role in shaping the context in which banks—both digital and traditional—execute their business plans. For example, with a regulatory framework that enables fully digital onboarding and operations, banks may be better able to reduce the costs of onboarding new customers. Or, in a market where the central bank puts in place mechanisms to support data flow, simplify credit scoring, and encourage open banking through common API standards, banks may find it easier to focus on data access to support their risk-management activities.”

Involve people within your institution who know what the regulations say. When working on innovative solutions, use these insights.

Speak to regulators and compliance officers to understand the potential regulatory roadblocks you might face as you bring your innovative solution to market.

Instead of viewing regulators as a barrier, make them assets. Seek their guidance through the process of innovating products that comply with applicable regulations.

Security Considerations

Hasty digital transformation can leave you vulnerable to security risks.

For example, as open banking becomes more popular, the number of third-party providers accessing a bank’s system will increase. There’s an obvious risk of unauthorized access to customer data and fraud.

Banks must standardize their security architecture and create a gateway for API call pre-validation.

PSD2 security requirements can adversely impact the customer experience, but RTS (Regulatory Technical Standards) provide the alternative of using behavioral biometrics.

Behavioral biometrics enable banks to identify potentially fraudulent transactions before they’re executed. According to a BioCatch case study, banks were able to flag 312 fraud attempts within a month, preventing over $300,000 in losses.

Collaboration with Fintechs ​

Collaborating with fintechs allows banks to play to their strengths and tap into the capabilities of fintech startups.

The Open Banking and Embedded Finance report states that companies are partnering with third-party providers to deploy advanced technologies.

Here are some critical areas where banks rely on third-party relationships:

According to the Innovation in Retail Banking 2021 report, banks specifically need innovative solutions for digital account opening, cloud deployment, open banking, and the speed of deployment of these solutions.

Here’s a quick overview of the level of partnership banks had with third-party providers in key areas as of December 2021:

Bigger banks might consider acquiring a fintech company instead of collaborating with them. Acquisitions allow banks to leapfrog into the future with innovative solutions. However, if an acquisition doesn’t seem viable, collaboration is a smart move.

Innovate with Blanc Labs​

Blanc Labs helps banks, credit unions, and Fintech companies automate processes and implement cutting-edge solutions powered by technologies like RPA, AI, and ML.

Banks are looking to partner with third parties to implement advanced technologies — the types of technologies we have extensive experience with.

If you’re looking to fast-track your journey of innovation in banking, we can help.

Book a discovery call to learn more about how we can help you implement the latest technologies for innovative banking solutions.


Digital Transformation Vs. IT Modernization: What’s the Difference?

Digital Transformation vs IT Modernization_Blanc Labs

In today’s dynamic business landscape, organizations face numerous challenges such as economic volatility, supply chain complexities, evolving work styles, competing for talent, fickle consumer trends, market commoditization, sustainability concerns, and more. As stated in Forrester Research’s 2021 report, “In the 2020s, the one constant will be disruption.” CIOs play a crucial role in navigating these challenges and driving business transformation. The ability to embrace disruption will set leaders apart from laggards in this ever-changing landscape. 

In this context, it is important for technology executives to make the choice between modernizing legacy processes or instituting a complete transformation of their systems. Yet, many organizations struggle to understand the difference.  

In this article, we explore both digital transformation and modernization and why transformation is mission-critical for modern businesses. 

Digital Transformation Vs. IT Modernization 

Digital transformation has a broader scope and touches  almost every facet of a business. IT modernization’s scope is limited to upgrading existing systems. 

Don’t get us wrong. Modernizing your tech stack  is just as critical to your business as transformation. The 2023 CEO Priorities survey shows that 51% of CEOs think low digital maturity/technical capabilities are barriers to creating business value with AI. As companies try to achieve digital maturity, they’ll go through multiple IT modernization iterations. 

Let’s dig into the differences between digital transformation and IT modernization.

Digital Transformation in Practice 

A digital transformation is meant to change or evolve some of the DNA of an organization such that a company is better equipped to compete in a constantly evolving  environment where customer expectations are changing at breakneck speed.  Technology can and should play a central role in a digital transformation but technology alone will not equip a company to operate effectively in this “new normal”, this requires a careful choreography of people, processes, and technology.  

In addition to being better equipped to meet evolving customer needs and expectations, digital transformation can be your doorway to scaling your business by entering new markets, eliminating or minimizing process inefficiencies, and becoming more agile. 

Domino’s is an excellent example of how digital transformation can change your company’s growth trajectory. Back in the day, the pizza chain was a brick-and-mortar restaurant that promised to deliver pizzas in under 30 minutes. 

Competitors started offering a similar service. So Patrick Doyle, the CEO of Domino’s in 2011, decided to create an app that allowed customers to order pizza within seconds. Currently, Domino’s is testing delivery via autonomous vehicles to stay ahead of delivery services like DoorDash and Uber Eats. 

Essentially, digital transformation involves a fundamental shift in various business aspects, like internal processes and your business model. The goal is to achieve sustained growth and become a more competitive business. 

Modernization in Practice 

Modernization is often confused with digital transformation, but they are not the same. Modernization focuses on building on top of existing systems. For example, you could implement cloud computing and AI without changing legacy processes. 

Suppose you’re a bank that is looking to improve its mortgage origination process. You currently require staff to enter customer details manually. Over the next quarter, you want to implement an automated loan origination system that eliminates almost all the mundane tasks in the origination process. 

For this, you’ll use technologies like AI, natural language processing (NLP), optical character recognition (OCR), and robotic process automation (RPA) through third-party integration or by building a new solution. These technologies will enable you to automate tasks like document processing and customer onboarding. 

These technologies can help you re-engineer the origination process to make it more efficient—without disturbing your underlying mortgage process or the business model. 

Why Digital Transformation Matters 

Digital transformation is right for you if you’re in a rapidly evolving market and want to stay ahead of the competition. 

Since digital transformation involves a more comprehensive change in your business’s model and operational processes, it can be resource and time-intensive, especially in the absence of first-hand experience on the multi-faceted nature of transforming an organization.  Let’s go back to our mortgage example to understand the difference between transformation and modernization.  Consider if the mortgage lender wanted to move from being a traditional, broker-based lender to a digital direct lender and the relative time, cost and complexity required to stand up a whole new business model.   

IT modernization is relatively less resource and time-intensive. Modernization is an excellent place to start if you’re struggling to meet customer expectations or efficiency targets because of outdated technology. 

Revamping your technological systems will help you reduce costs, operate more efficiently, and scale your business. 

 Digital transformation matters to two types of businesses: 

  • Businesses that want to stay ahead of the curve by implementing the latest technologies in a fast-evolving market. 
  • Businesses that need to survive in a market where other businesses failed to implement new technologies on time. 


Digital transformation isn’t easy for either of these businesses to implement without an experienced team—only about 30% of companies navigate digital transformation successfully. 

However, once you’ve successfully completed digital transformation, your business stands to benefit on multiple fronts. 

The pandemic caused the most recent surge in digital transformation. Businesses were forced to adapt to an environment where employees prefer remote or hybrid work models, but they benefited greatly from the changes that followed. 

That’s exactly why more businesses are excited about digital transformation. In fact, according to the World Economic Forum, the combined value of digital transformation to society and industry will exceed $100 trillion by 2025. 

What Digital Transformation Benefits_Blanc Labs

Here are some benefits of digital transformation that highlight why it can be a game changer for your business this year: 

Improves Customer Experience 

Customer expectations have risen significantly over the past decade. They prefer businesses that offer a great experience in all customer-facing aspects. 

Enable  Data-Driven Decision Making 

Data analytics is one of the most critical capabilities you gain from digital transformation. With modern tech, it is typically much easier to securely access and manipulate data that can be used to unlock valuable insights about customers, competitors, and internal processes. 

Better Resource Management 

Digitally-powered businesses use hundreds of apps and store data in multiple places. The lack of interoperability and data silos can make business hard. Digital transformation involves integrating these digital tools, allowing you to consolidate dispersed data sets and apps. 

Improves Team Collaboration 

Adding the right digital collaboration tools to your toolkit can be critical to building a collaborative environment in the workplace. They allow teams to collaborate outside the office space or country. If your team is spread across time zones, you can use async communication tools for better collaboration. 

Increases Scalability 

Digital transformation gives you the flexibility to grow faster. It can help reduce time to market and make product or service improvements faster. You can also serve clients in a larger geographic area and communicate with your clientele more effectively. 

What Does it Take to Create Digital Transformation? 

Digital transformation needs more than just technical expertise. It’s a massive undertaking where multiple factors contribute to your success. 

Here are things to focus on when transforming your business digitally: 

Digital First Culture 

Transforming your business requires agility and an attitude to pursue the latest technologies proactively. Building a digital-first culture, where employees proactively look for opportunities to use digital solutions to help the business achieve its goals, is critical to successfully transforming your business. 

Culture is a critical part of Gartner’s ContinuousNEXT approach to digital transformation. The Gartner experts who coined the term cite culture as a major barrier — 46% of CIOs identify culture as the most significant barrier to digital transformation. 

A report by MIT and Capgemini explains that focusing on the following seven cultural attributes can be vital to building a digital-first culture: 

  • Innovation: The prevalence of behaviors that support taking risks, thinking disruptively, and exploring new ideas. 
  • Digital-first mindset: A mindset where everyone, by default, thinks of digital solutions first. 
  • Customer-centricity: The use of digital tools to improve customer experience, expand the customer base, and co-create new products. 
  • Open culture: The extent of partnership with third parties, including vendors and customers. 
  • Agility and flexibility: Decision-making speed, dynamism, and your company’s ability to adapt to evolving market demands and technologies. 
  • Data-driven decision-making: The use of data to make better decisions. 
  • Collaboration: The strategic creation of teams to make the best use of the company’s human resources. 

Change Management 

 Often, the most overlooked aspect of a digital transformation is the amount of change management and capability development required to enable team members to excel in their new or evolved roles. One takeaway that we’ve learned from our experience is the need to have a strong leadership commitment and a high level of conviction and alignment across the senior leadership team on the transformation objectives, priorities, and plan to undertake the transformation effort at an organization. . 

 When communicating a change, remember that the benefits of change may be evident to you. Yet, your team might see change as a signal to step out of a workflow they’re comfortable with and learn new skills. 

Resistance will snowball when one employee who feels that way communicates with colleagues. Change management can help prevent or at least minimize this resistance to change. 

Here are three critical elements of managing change: 

  • Plan before you start: Change management should start before digital transformation. Risk analysis and assessing the organization’s readiness are great ways to lay the foundation for your change management strategy. They involve identifying organizational attributes like culture and characteristics of change. 
  • Change management: This is where you go from planning to execution. You keep communicating your digital transformation plans, address concerns regarding the plan, and train employees. 
  • Feedback: Change management doesn’t end after executing your change management strategy. Seek feedback from employees about the change. If you see reluctance, take corrective actions to ensure resistance doesn’t snowball. 

Data-Driven Organization 

Digital transformation is highly data-driven. Managing data — storing and processing data and ensuring data integrity — are one of the first things you’ll need to work on when you start the digital transformation process. 

Data provides a unique insight into your operations. Once you’ve achieved a higher level of operational transparency and gained a deeper understanding of core business processes using data, you’ll be able to identify barriers to digital transformation. Data will also enable you to improve and optimize your transformation strategy continuously. 

Companies like Airbnb and Amazon hold massive volumes of data. These companies are great examples of data-driven organizations. This data allows them to personalize customer experiences and stay one step ahead of the competition. 

Emphasis on Quality 

When you’re adamant about delivering quality, you’ll need to ask yourself — is the business prepared to ensure a high level of quality during the digital transformation journey? 

Going all in at once is a bad idea. Digital transformation requires an incremental approach where you plan, test, and validate ideas every step of the way. This will allow you to stay prepared for potential pitfalls and optimize your strategy as you go. You’ll avoid complete disasters, which can result in loss of money, trust, and reputation. 

Taking an incremental approach makes your digital transformation journey smoother and ensures you maintain high quality throughout the transformation process. 

Begin Your Digital Transformation Journey 

Digital transformation can be challenging to navigate without an experienced partner. 

At Blanc Labs, we understand each organization has unique needs. We offer advisory and consulting services to gain a complete understanding of your most pressing challenges and help you start your digital transformation journey. 

Book a discovery call with us today to learn more. 


Understanding the Potential of Generative AI in Healthcare

GenerativeAI in Healthcare_Blanc Labs

Generative AI is artificial intelligence that can create things like text, images, and music. But how would someone use generative AI in healthcare? 

This technology has a long list of use cases in healthcare across the board, from drug discovery to improving patient outcomes. 

The World Health Organization (WHO) estimates a deficit of 10 million health workers by 2030. Generative AI can play a significant role in addressing this deficit. 

As a World Economic Forum article aptly explains: 

“Generative AI isn’t just a passing trend; it’s a rapidly evolving ecosystem of tools growing in popularity showing huge potential to revolutionize healthcare in ways we’ve never seen before. 

In this article, we explore generative AI’s use cases in healthcare and their implications. We also help you assess your readiness to use generative AI at your healthcare facility. 

Generative AI in Healthcare: Potential Use Cases 

The healthcare industry is already thinking of generative AI’s many use cases. In fact, many healthcare experts believe generative AI can transform healthcare. 

According to an Accenture report, 98% of healthcare providers and 89% of healthcare payer executives believe that generative AI developments will bring a new era of healthcare intelligence. 

Below are some exciting generative AI use cases for the healthcare industry.

Drug Discovery 

You’re probably familiar with how time-consuming and expensive the drug discovery process is — a vaccine takes an average of 10 to 15 years to develop. 

Generative AI can speed up this process in various ways, such as: 

  • Identifying new drug molecules: Creating new drug molecules that can potentially be used to develop drugs faster using a large dataset consisting of chemical structures and properties. 
  • Testing the efficacy and safety of drug candidates: Drug discovery involves testing plenty of viable drug candidates for efficacy and safety. Generative AI algorithms can help identify or screen candidates based on very specific criteria that may exist in multiple data sets. 
  • Creating virtual compounds: AI can create and test virtual drug compounds in a computer simulation, which is cheaper than a lab. 
  • Identify target-specific molecules: Generative AI can study a molecular structure’s properties from a database to identify new molecules that can better deal with a specific target. 

Disease Diagnosis 

Generative AI can ingest a large dataset of medical images to learn the identification of patterns that indicate a specific disease. 

For example, you can train AI using a large volume of images that show glaucoma or eyes with abnormalities and differentiate them from healthy eyes. This enables AI to learn how glaucoma looks different from other eye infections. 

Medical professionals can use AI to make more timely and accurate diagnoses. AI also helps doctors create treatment plans faster, which helps improve patient outcomes. 

Effectively, AI can help you provide the right treatment at the right time. AI-powered, personalized patient care reduces treatment misallocation and optimizes the associated resource utilization. 

Patient Care and Treatment Plans 

Generative AI can help create personalized treatment plans based on the patient’s medical records, lifestyle patterns, and genetic information. 

You can also offer patients more engaging treatment, improving the plan’s effectiveness. For example, generative AI can auto-trigger notifications via email or text to help patients stick to their treatment plans, improving patient outcomes. 

Medical Imaging 

Generative AI can help enhance medical images. A generative AI model can study a large dataset of medical images of a specific body part. 

Once the algorithm is trained using this data, you can feed a medical image, and the algorithm will generate a high-resolution image for you. 

Think of a CT scan of the lungs, for example. When doctors feed this image into an AI algorithm to make it more high-resolution, they’ll be able to spot otherwise unnoticeable differences in the lungs that could be indicative of a disease. 

Reduce the Friction of Data Access 

Unsupervised generative AI is a type of generative AI that learns from unstructured data that lacks labels and categorization. 

When you have a massive dataset and no guidance for analyzing it, unsupervised generative AI can help. 

Interpreting unstructured data can help with various healthcare-related tasks. For example, it helps summarize key facts from unstructured data in EHR (electronic health records), medical notes, and medical images. 

This eliminates the need to spend time reading through various types of records you handle on a daily basis. You can quickly review the summary and start testing, diagnosis, or treatment. 

Reduce Physical Burnout 

According to Dr. Robert Bart, chief medical officer at the University of Pittsburgh Medical Center, lessening physicians’ documentation burden is a top priority for many hospital technology leaders. 

Clinicians spend roughly five to six hours a day taking notes in EHRs. This can cause burnout. Generative AI can summarize patient and treatment information. It can also review the patient’s medical records and provide a summary, making the physician’s work easier. 

For example, think about how tools like have made transcribing and synthesizing notes from virtual meetings a dream.  The same can be achieved during a patient consult. 

Generative AI can significantly reduce administrative workload. This not only addresses the problem of shortage of healthcare workers but also helps minimize errors — up to 50% of all medical errors in primary care are caused because of administrative reasons.

Automated Health Assistance 

Medical professionals often spend a lot of time responding to emails or scheduling appointments. Creating a virtual health assistant (i.e., an AI-powered chatbot) can help patients get basic information and schedule appointments themselves. 

Medical Research 

Sharing patient data for research and healthcare while ensuring data security and privacy. New drugs and medical devices require years of research and billions of dollars before they pass clinical trials and get regulatory approval. 

Gen AI can possibly be used to partially simulate clinical testing. This can help bring drugs to market faster and at a lower cost. 

Five-Step Approach to Assess if You Are Ready for Healthcare Generative AI 

Assessing your readiness before implementing generative AI ensures your business and team aren’t overwhelmed by its complexities. 

Jumping in with complete awareness gives you the confidence to derive generative AI’s full benefits. Here’s how you can assess your business’s readiness for generative AI: 

  1. Start by learning about AI technology: Learn about the AI tech you want to use. What problem will it solve for your business? How good is the tech at solving your problem? 
  2. Identify major areas that need AI intervention: Does the AI tech solve problems related to drug discovery, disease diagnosis, or burnout? 
  3. Barriers to adoption: Will something stand in the way of you successfully implementing this tech? It could be your budget or lack of buy-in from employees who fear they might lose their jobs. 
  4. Prioritize: Study the business dynamics around entering high-priority categories with this tech. Do you expect any disruption during or after implementing the AI tech? 
  5. Find potential for expanding the scope of the solution: Can you extend the scope of the AI solution to include more than just the primary use case? If you’re creating a healthcare assistance solution, you might consider expanding the solution’s scope by including patient education. 

This five-step approach will help you understand your readiness for implementing AI solutions for your healthcare business. 

The Practical Implications of Using Generative AI in Healthcare 

Microsoft and Epic are already testing generative AI for EHRs. Google is developing its own language model Med-PaLM 2, which aims to help diagnose complex diseases. 

With multiple big companies already making a move towards using generative AI in healthcare, here are some implications we can expect to see in healthcare: 

Clinicians Will Spend Less Time on Clinical Documentation 

Generative AI will eliminate the need for clinicians to spend too much time on documentation. 

Major clinical documentation companies are starting to integrate generative AI into their solutions. Doing so can improve the solution’s documentation speed and accuracy. 

For example, Microsoft’s documentation company Nuance recently integrated GPT-4 into its clinical note-taking solution. 

Similarly, Google has partnered with Suki, a documentation company that launched a generative AI voice assistant. Suki can auto-generate clinical notes during a conversation and enter those details into the note. 

AI Will Emulate Medical Decision-Making 

Generative AI can’t guarantee a future outcome. However, it can “predict” the next best idea when generating text to illustrate a concept. 

This can help solve various problems, including making medical decisions. In fact, generative AI uses a similar process to make decisions as doctors. 

Doctors learn medical science in college and then from research papers, medical journals, and experience. Similarly, AI learns from digital resources available on the internet. 

When a patient walks in, doctors use their knowledge or search through relevant assets to understand and treat a patient’s symptoms. AI tries to find relevant text using billions of parameters. 

Doctors predict possible causes and try to narrow things down by comparing diagnoses. Generative AI compares words and sentences, weighs its options, and finds the best possible match from the available resources. 

The most significant shortcoming is that AI currently can’t interrogate the patients or order tests for more context. This will likely change in the near future. 

Medical Errors Will Reduce Significantly 

Medical errors are a major cause of death in U.S. hospitals. A New England Journal of Medicine paper suggests that close to 1 in 4 patients in a hospital may experience some sort of harm during their time in the hospital. 

The future generations of AI will be able to monitor medical professionals and trigger alerts in real time when they’re about to commit an error. This includes errors committed when writing a prescription or during surgery. 

There has been promising progress toward AI-based video surveillance. Installing these technologies in ICUs and operation theaters significantly improves patient outcomes and minimizes errors. 

You can use an AI-based solution with a patient monitor to monitor the patient’s condition. The AI tool can collect this data and over time, learn from this data to become better at detecting abnormalities. 

However, it’s also important to remember that AI can make mistakes. Moreover, you can’t quite determine how AI got the data it used to generate the results it did. Keeping these limitations will ensure you’re able to make the best use of generative AI in healthcare. 

The Discovery of New Use Cases Will Continue 

Generative AI is still in its infancy. As it evolves, healthcare professionals will be able to find new use cases to improve efficiency and minimize errors. 

The problem? AI is evolving way too fast. You’ll need constant support from a qualified partner to determine how you can succeed. 

Blanc Labs can help you implement customized, turnkey AI solutions that will provide the support you need on your digital transformation journey. Book a discovery call with Blanc Labs to learn more about how generative AI can help your healthcare business. 



Healthcare Interoperability: Challenges and Benefits

Challenges and Benefits Health Interoperability

The American Recovery and Reinvestment Act (ARRA) of 2009 provided hospitals and health professionals incentives to use electronic health record technology. Healthcare organizations quickly moved healthcare records to digital applications, providing an opportunity to use this data cohesively through healthcare interoperability. 

ARRA has been the driving force behind the digitization of healthcare records in the recent past. The problem? Software vendors developed various applications for the healthcare industry. The result was data silos stored in disparate systems. 

Healthcare interoperability is a step towards developing a digital ecosystem for the healthcare industry, where data can be exchanged and accessed securely without  boundaries. 

What is Healthcare Interoperability? 

 Interoperability removes the barriers in information exchange introduced by differences in technology, architecture, and vendors. 

Seamless access to healthcare data is critical. The inability to access healthcare records during an emergency can result in adverse outcomes. 

Moreover, information blocking can result in penalties of up to $1 million per violation. 

Keeping health data secure is just as important as the ability to share it. That’s why healthcare interoperability requires a careful approach. The combined use of APIs (Application Programming Interfaces) and information standards like FHIR and HL7 can help healthcare companies make the best use of electronic records while ensuring data integrity. 

Healthcare interoperability allows clinicians to provide better care and coordinate with other clinicians. It provides clinicians and other healthcare providers with a standardized way to collect and report public health data. 

Collectively, these factors can improve patient outcomes and safety, minimize the risk of error, and increase the efficiency of internal processes. 

Levels of Healthcare Interoperability 

The Healthcare Information and Management Systems Society (HIMSS) has defined four levels of healthcare interoperability. Each level represents a type of data exchange. 

Foundational Interoperability 

Foundational interoperability (or simple transport) is the most basic type of interoperability. A system transfers data to another system without interpreting or changing its format. 

For example, you download a patient’s public health record and manually enter those details into your proprietary software. 

Structural Interoperability 

Structural interoperability (or structured transport) involves interpretation. Systems exchange data and, when needed, convert it to a standardized format for interpretation. 

The information uses a standard syntax and organization, so it’s easier for the receiving system to detect and interpret specific fields. 

FHIR and HL7 provide structural interoperability, allowing you to move information across systems seamlessly. 

Semantic Interoperability 

Exchanging and interpreting data with entirely different data structures requires semantic interoperability (or semantic transport). 

Suppose you receive a scanned image of a patient’s medical report. The information in this image must be converted into text fields before it can be imported into your system. 

Extracting the information from one system, structuring it so that another system understands the extracted information, and automatically filling out the right data in the right fields requires artificial intelligence (AI). 

A combination of technologies like optical character recognition (OCR), robotic process automation (RPA), and AI can help achieve full semantic interoperability like so: 

  • OCR extracts the information from the image: The information in the patient’s report like their name and blood group is extracted. 
  • AI-based technologies like NLP and machine learning (ML) help interpret the extracted information: The information may not always be in a standard format. For example, the numbers in your blood report may be written as 10^9 or 109. NLP will help the system understand that both of these mean the same thing. 
  • RPA populates the relevant data in the recipient system: Once the system interprets this information, RPA automatically adds this information to the recipient system. 

Organizational Interoperability 

Organizational interoperability is the highest level of interoperability. 

It facilitates sharing and interpreting healthcare data securely, seamlessly, and in a timely fashion between organizations, entities, and individuals, with governance, policy, social, legal, and organizational considerations factored in. 

Organizational interoperability is the goal. But most healthcare companies are still working on achieving foundational and structural interoperability. 

Once organizations have achieved lower levels of interoperability, they’ll have a strong foundation for achieving organizational interoperability and other ways to improve health data exchange. 

Navigating the journey from foundational to organizational interoperability is fraught with challenges, but these can be overcome with careful planning and strategizing. Read more here 

What is FHIR? 

Fast Healthcare Interoperability Resources (FHIR) is a healthcare data standards framework developed by HL7 (Health Level 7). The FHIR provides a standard framework to make transferring healthcare data between systems easier. 

FHIR consists of resources like health data formats and elements (such as conditions and medications) that you can exchange easily. It also provides standardization for APIs. 

Modern healthcare benefits from FHIR in multiple ways. It facilitates exchanging information with legacy applications, but that’s not the only reason to use FHIR. 

The Blue Button 2.0 API, which allows accessing healthcare information, is based on FHIR. The FHIR standards framework is a key component of the United States’ national interoperability roadmap. 

If your healthcare business receives payments for Medicare or Medicaid, using FHIR for interoperability is critical. 

Data from an Engineer Group survey commissioned by Change Healthcare suggests that only 24% of healthcare companies were using FHIR APIs at scale in 2021. However, the research suggests widespread adoption by 2024. 

As more healthcare providers start using FHIR APIs, they’ll be able to use and provide patients with a richer set of functionalities. 

4 Challenges with Healthcare Information Exchange 

The current low rate of interoperability is a result of the challenges associated with healthcare information exchange. Below are four of the most pressing challenges that stand in the way of healthcare organizations achieving interoperability.

Inconsistent Data 

Healthcare organizations generate data from multiple, disparate sources. These sources typically store data in the database in various formats and data types that are incompatible with each other. 

When systems exchange incompatible data types, the recipient system can’t interpret the information. For example, medical records may contain the patient’s medical history and treatment plan. The recipient system must interpret this information to be able to use it. 

Maintaining Client Data Confidentiality 

Ensuring the confidentiality of patient health records is critical to maintaining a good reputation and, more importantly, complying with HIPAA (Health Insurance Portability and Accountability Act). 

 Electronic health records (EHR)  need a secure mechanism to validate requests for patient information. 

Many providers use systems that may or may not be compatible with EHR products, which can potentially result in a breach of regulations like HIPAA. 

Once the ONC’s Cures Act Final Rule comes into force, healthcare providers will need to comply with its new training and certification requirements too. 

Personal health information (PHI) breaches can be a recipe for losing reputation and heavy penalties. 

Conflict of Interest 

Not all businesses want to share patient data because you’ll often need to share information with a direct competitor. 

For example, if you’re a hospital, you’ll understandably be reluctant to share patient data with urgent care clinics. 

Regulations are the best solution to this challenge. The Cures Act has various information-blocking provisions that will compel healthcare providers to provide information when appropriate. 

Cost of Hiring an Interoperability Specialist 

Achieving interoperability is expensive because it requires specialists that dedicate their time to maintain interoperability. 

Of course, this person needs the right qualifications and experience handling interoperability-related tasks. 

If you make some rough calculations, you’ll see just how expensive hiring this specialist can be. The cost makes providers, especially smaller healthcare businesses, rethink the feasibility of interoperability. 

The solution to this problem is simpler than the previous ones. Instead of hiring a person, you can invest in an automated interoperability system that takes care of most tasks. 

An automation system costs significantly less than hiring a specialist in the long term. 

5 Benefits of Healthcare Interoperability 

The benefits of healthcare interoperability far outweigh the cost of addressing the challenges. Here are the five benefits healthcare interoperability offers.

Improves Patient Outcomes and Experience

Healthcare interoperability isn’t just a regulatory burden. It’s an asset you can build to improve patient outcomes and experience. 

As life expectancy rises, interoperability will prepare you for value-based patient care. Real-time access to a patient’s medical history allows you to get a deeper insight into the patient’s condition and minimize medical errors. 

Data access also reduces duplication of efforts. Since you’ll have the information about diagnosis, tests, and results, you can directly start working on developing a treatment plan or running other tests. 

You’ll know about the patient’s allergies and health plan before starting treatment so that you can provide appropriate advice. 

These factors collectively improve the patient’s experience and allow you to provide better care. 

Reduces Cost of Care 

Interoperability reduces the cost of care in multiple ways: 

  • Streamlines care delivery: Better coordination among healthcare providers streamlines care delivery. You won’t have to repeat tests, and you’ll have the information about the previous diagnosis and treatment. 
  • Minimizes errors: Interoperability reduces the cost of care by minimizing medical errors. 
  • Increased productivity: Your administrative staff won’t have to reenter the same data over and over once you’ve achieved interoperability. Your team saves time on manual data entry when you use technologies like intelligent document processing (IDP) to convert physical documents into digital files. 

Collectively, these factors can help reduce the cost of care by a good margin. You can transfer these savings to your patients to offer them more value at a lower cost. 

Keeps Patient Data Secure 

Patients trust that their data is safe with healthcare providers. Compromising this data’s integrity can result in a loss of reputation. Ensuring data integrity is also a compliance requirement. 

Hundreds of electronic medical records are compromised daily. As many as 54,396 individuals were affected just by a single breach at the NewYork-Presbyterian Hospital on March 20, 2023. 

Your systems need to be HIPAA-compliant. The best interoperability partners are experienced in creating compliant interoperability solutions, which is reassuring when implementing a complex technological solution with legal implications. 

Contributes to Research 

The data you collect during regular business, like diagnosing, testing, and treating patients can be an asset for public health researchers. 

Interoperability allows researchers to request data from your systems for studies in various medical fronts like epidemiology and pathology. 

This helps build a good reputation. You can add the fact that you share data with scientists to contribute to society and build goodwill for your healthcare business. 

Minimizes Burnout 

Digital transformation generally makes processes faster and easier. But the situation with EHR adoption is a little different. 

The administrative load on physicians has increased significantly because of compliance requirements and disparate solutions used by clinicians. 

That’s where interoperability helps. It allows you to automate mundane labor-intensive tasks like data entry. 

With less time spent on time-consuming and repetitive tasks, your administrative staff won’t reel under the pressure of EHR compliance requirements. Addressing burnout also reduces the probability of human error. 

Start Your Interoperability Journey with Blanc Labs 

Achieving structural interoperability offers various benefits. Selecting a partner with extensive experience managing APIs is critical to reaping the full benefits of structural interoperability and frictionless implementation. 

Blanc Labs are experts at building standards-based interoperability solutions that enable healthcare organizations to improve patient outcomes, enhance efficiency and achieve seamless integration within the health ecosystem. 

Book a discovery session with Blanc Labs to learn how we can help your healthcare business achieve interoperability. 


Navigating the Healthcare Interoperability Journey

The journey of navigating healthcare interoperability is a critical one, and an incredibly complex endeavor. Healthcare organizations must tackle big tasks like accessing exchange networks, mapping messages across systems, and integrating with multiple data sources while also operating within tight compliance rules. It can be especially daunting for executives tasked with ensuring successful implementation. If this describes you or someone on your team, don’t worry—there are ways to ensure success as you undertake the process of achieving healthcare interoperability. 

In this article we explain what it means to embark on an interoperability journey and how best to implement it throughout an enterprise organization.  

Stage 1: Strategy and Roadmap

Beginning the healthcare interoperability journey involves addressing key pain points, such as: 

  1. A lack of common standards and communication protocols between existing health systems like Electronic Medical Record (EMR), Laboratory Information Systems (LIS), etc. 
  2. Limited IT budgets and minimal underlying infrastructure 
  3. A shortage of interoperability-focused resources 

To tackle these challenges, the first step is to create a well-defined strategy, followed by a comprehensive gap analysis and a dynamic roadmap aimed at ensuring regulatory compliance and achieving seamless integration of healthcare data. A crucial aspect of this journey is addressing the CMS (Centers for Medicare and Medicaid Services) mandate that requires healthcare organizations to adopt and implement interoperability standards.  

At this stage of the interoperability journey, organizations move from having disconnected data systems to making data organized and manageable. Applications of interoperability at this stage include patient-centered care

The importance of securing curated and standardized health data

Securing curated and standardized data is crucial in ensuring that information is both organized and meaningful, particularly in the context of patient-centered care. By utilizing a data curation process, healthcare providers can effectively gather, annotate, and maintain relevant datasets that accurately represent patients’ medical histories, conditions, and preferences. This process often involves removing inconsistencies or inaccuracies, as well as integrating data from various sources into one unified platform. Standardizing this data in accordance with industry regulations or established protocols, such as the International Classification of Diseases (ICD) or Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), enables seamless communication and the exchange of information.  

Furthermore, the implementation of advanced security measures, including encryption and robust access controls, helps protect sensitive patient information from unauthorized access or potential breaches, adhering to privacy standards like the Health Insurance Portability and Accountability Act (HIPAA). Altogether, the rigorous curation, standardization, and security of data serve as foundational elements in the journey to interoperability.  

Stage 2: Validating Strategy

The journey towards interoperability maturity is a complex and ongoing process, requiring healthcare organizations to regularly validate their strategies and roadmaps, align budgets with evolving business needs and technological advancements, and comply with CMS mandates. Achieving interoperability maturity involves focusing on core aspects such as:  

  • Enabling seamless communication and coordination between disparate systems 
  • Instituting a robust data management plan with accurate data mapping 
  • Leveraging cutting-edge API technologies to address diverse use cases efficiently

Organizations must stay ahead of the curve by continuously assessing and refining their interoperability efforts with industry best practices and regulatory requirements as benchmarks. This iterative approach ensures that organizations can consistently drive improvements in care delivery, patient satisfaction, and long-term healthcare outcomes. 

By this stage in the interoperability journey, companies graduate from simply having organized data systems to making them data analytics ready. This enables organizations to track patients over a longer period and participate in integrated healthcare.

Stage 3: Developing and end-to-end ecosystem for healthcare interoperability

The last stage in the healthcare interoperability journey is about addressing the alignment between business and technology objectives. A fundamental aspect of this stage is obtaining leadership buy-in, thereby empowering organizations to extend their interoperability initiatives beyond what is mandated by regulatory and industry requirements.  

By focusing on enabling a comprehensive end-to-end solution for interoperability, organizations can leverage the potential of emerging standards, such FHIR, to facilitate seamless intra- and cross-organizational data exchange. Additionally, investing in the development of an Application Programming Interface (API)-driven ecosystem allows organizations to foster a highly connected, flexible, and scalable technology infrastructure that promotes innovative and improved patient-centric care services.  

In the final stage of the healthcare interoperability journey, organizations are in the position to develop data driven applications (e.g predictive analytics, advanced reporting, population health management, etc.) using the latest technologies like AI to improve patient outcomes, enhance operational efficiency & increase profitability. 

Get Started on your Healthcare Interoperability Journey with Blanc Labs 

Interoperability can seem daunting, especially when trying to make sense of the entire journey. However, if approached methodically and incrementally with a well-thought-out strategy and roadmap, the end result can be a more secure, efficient, and user-centric system. Starting with developing the vision for an ecosystem that works for both you and your customers should give you confidence for making investments in interoperability technologies.  

To understand how the interoperability journey will apply to your organization, simply speak to an expert from Blanc Labs today. 


Using RPA in Banking

All banking or financial institutions can relate to the struggle of managing piles of structured and unstructured data daily. This task requires repetitive and manual effort from your employees that they could otherwise dedicate to high-value work. It can also be time-consuming and prone to errors, ultimately hampering your bank’s customer experience. Fortunately, automation technologies are proving to be a boon for the finance sector. 

 The finance domain is experiencing a major transformation, with banking automation and digitization at the forefront. According to a study by McKinsey, machines will handle between 10% to 25% of banking functions in the next few years, which can free up valuable time and resources for employees to focus on more strategic initiatives. 

What is Robotic Process Automation (RPA)?

RPA is an automation technology governed by structured inputs and business logic. RPA in banking is a powerful tool that can automate repetitive and time-consuming tasks. It allows banks and financial institutions to gain a competitive advantage by automating routine tasks cost-effectively, fast, and without errors.  


Banks, credit unions, or other financial institutions can set up robotic applications to handle tasks like capturing and analyzing information from documents, performing transactions, triggering responses, managing data, and coordinating with other digital systems. The possibilities for using RPA in finance are innumerable  and can include a range of functionalities such as generating reports, sending auto emails, and even auto-decisioning.


How RPA works 

Robotic Process Automation works by automating repetitive and routine tasks that are currently performed manually. Software robots, also known as ‘bots,’ are designed to mimic human actions and interactions with digital systems. These rule-based bots can be configured to perform specific tasks, such as document processing, data entry, transaction execution, complete keystrokes, and more. 

Once a bot is configured, it can be triggered to run automatically or on a schedule, freeing up human resources to focus on customer service or other higher-value or strategic activities. The bot interacts with the relevant systems and applications, capturing and analyzing data, navigating systems, and automating workflows as needed. 

One of the key advantages of RPA in finance is that it is non-intrusive, meaning that it operates within existing systems and processes, without requiring any changes to the underlying infrastructure. This means that no changes are made to the underlying applications. RPA bots perform tasks in a similar manner  to employees- by signing into applications, entering data, conducting calculations, and logging out. They do this at the user interface or application surface layer by imitating mouse movements and the keystrokes made by employees. 

This makes it easier to implement and reduces the risk of disruption to existing operations. As per Forbes, RPA usage has seen a rise in popularity in the last few years and will continue to see double-digit growth in 2023.Many people use the terms ‘RPA’ and ‘Intelligent Automation’ (IA) interchangeably. Both are banking automation technologies that improve efficiency, but are they the same?  

Are RPA and Intelligent Automation the same? 

No, RPA is not IA and IA is not RPA. While RPA is a rule-based approach for everyday tasks, intelligent automation uses Artificial Intelligence (AI) and Machine Learning (ML) technologies to automate more complex and strategic processes. IA encompasses a wide range of technologies which includes RPA. IA enables organizations to automate not just manual tasks but also decision-making processes and allows for continuous improvement through self-learning.  

A combination of IA and RPA can unlock the true potential of banking automation. When RPA is combined with the powers of AI, ML, and natural language processing, it dramatically increases the software’s skills to execute advanced cognitive processes like understanding speech, carrying out conversations, comprehending semi-structured tasks such as purchase orders, invoices and unstructured documents like emails, text files and images. 

Thus, RPA and its combination technologies are fully capable of taking your banking and financial business to new heights.  

What are the benefits of RPA in Banking? 

The global RPA market is projected to grow at a CAGR of 23.4%, from $10.01 Billion in 2022 to $43.2 Billion in 2029. Evidently, more industries worldwide are realizing the importance of RPA. Here are some benefits of using RPA in banking and financial institutions. 

Improved Scalability 

Robots can work faster and longer than humans without taking breaks. RPA can also be scaled to meet changing business needs, making it an ideal solution for organizations that are looking to grow and expand their operations and provide additional services. 

Enhanced Compliance and Risk Management 

RPA can help banks and financial institutions improve their compliance and risk management processes. For example, the software can be configured to monitor transactions for potential fraud and to ensure compliance with regulatory requirements. It can also inform the bank authorities in case any anomaly is found. 

Improved Customer Service 

RPA can enable faster and more personalized service to customers. For example, the software can be configured to handle routine customer inquiries and transactions, reduce wait times and improving the overall customer experience. 

Increased Efficiency 

RPA can automate repetitive and manual tasks, redirecting human resources to other higher-value and strategic activities. This can result in faster processing times, improved accuracy, and reduced costs. According to a study by Deloitte, banking institutions could save about $40 million over the first 3 years of using RPA in banking.  

Better Data Management 

RPA can automate the collection, analysis, and management of data, making it easier for banks and financial institutions to gain insights and make informed decisions. This means faster account opening or closing, loan and document processing, data entry, and retrieval. 

Top Use Cases of RPA in Banking 

RPA can be applied in several ways in the banking and finance industry. Here are some examples of RPA use cases in banking and finance: 

Accounts Payable 

RPA can automate the manual, repetitive tasks involved in the accounts payable process, such as vendor invoice processing, field validation, and payment authorization. RPA software in combination with Optical Character Recognition (OCR) can be configured to extract data from invoices, perform data validation, and generate payment requests, reducing the risk of errors and freeing up human resources.  This system can also notify the bank in case of any errors. 

Mortgage Processing 

Mortgage processing involves hundreds of documents that need to be gathered and assessed. RPA can streamline the mortgage application process by automating tasks such as document verification, credit checks, and loan underwriting. By using RPA to handle routine tasks, banks, and financial institutions can improve processing time, reduce the risk of errors, and enhance the overall customer experience.  

Fraud Detection 

According to the Federal Trade Comission (FTC), banks face the ultimate risk of forgoing money to fraud, which costs them almost $8.8 billion in revenue in 2022. This figure was 30% more than than what was lost to bank fraud in 2021 .  RPA can assist in detecting potential fraud by automating the monitoring of transactions for unusual patterns and anomalies. Bots can be configured to perform real-time ‘if-then’ analysis of transaction data, flagging potential fraud cases as defined for further investigation by human analysts. 

KYC (Know Your Customer) 

RPA can automate the KYC onboarding process, including the collection, verification, and analysis of customer data. RPA software can be configured to handle routine tasks such as data entry, document verification, and background checks, reducing the risk of errors and faster account opening, thus resulting in enhanced customer satisfaction. 

Thus, using RPA in your bank and financial institution can not only save time and money but also boost productivity. Banking automation gives you a chance to gain a competitive edge by leveraging technology and becoming more efficient. 

Blanc Labs Automation Solution for Banks 

Blanc Labs helps banks, credit unions, and financial institutions with their digital transformation journey by providing solutions that are RPA-based. Our services include integrating advanced automation technologies into your processes to boost efficiency and reduce the potential for errors caused by manual effort.  

We offer a tailored approach that combines RPA, ML, and AI to automate complex tasks, such as mortgage processing and document processing, allowing you to conserve resources, speed up decision-making  and provide quicker and improved financial services to your customers. 

If your bank processes a huge amount of data everyday, we can help you. Book a discovery call with us and let us explain how we can increase the efficiency of your bank’s core functions. Our team will analyze your current processes and propose a tailor-made automation solution that can operate seamlessly and in conjunction with your existing systems. 


How to Automate Loan Origination Systems

Loan Origination Automation_How to_Blanc Labs

Loan origination automation is critical because the loan origination process is labor-intensive and prone to human error. 


The process takes expensive human capital that you can dedicate to other, more strategic tasks. It’s also prone to human error, which increases your costs and puts your reputation at risk. 

Automating the process takes humans out of the equation, minimizing the cost of human capital and the risk of human error. 

In this article, we explain how to automate the process using an automated loan origination system. 

What is the Loan Origination Process? 

Loan origination is the process of receiving a mortgage application from a borrower, underwriting the application, and releasing the funds to the borrower or rejecting the application. 

When a customer applies for a mortgage, the lender initiates (or originates) the process necessary to determine if a borrower should be lent funds according to the institution’s policies. 

The process is extensive and takes an average of 35 to 40 days. The origination process involves five steps, as explained below. 


Prequalification is a screening stage. This is where lenders look for potential reasons that can adversely impact a borrower’s capability to repay the loan. 

Typically, lenders look for things like: 

  • Income: Does the borrower make enough money to be able to service the loan payments, and is that income consistent? 
  • Assets: Should the borrower’s income stop for some reason; do they have enough assets to remain solvent given their existing liabilities? 
  • Debt: Is the borrower overleveraged? Are the debts secured or unsecured? 
  • Credit record: Has the buyer made loan payments on time in the past? 

These factors help the lender determine if they should spend time processing the application further. 

Preparing a Loan Packet 

Lenders create a packet (essentially a file of documents) for prequalified borrowers. 

The packet includes the borrower’s documents, including KYC, financial statements, and other relevant documents that provide an overview of the borrower’s debt servicing capability. 

Lenders also include documents that highlight the reasons that make an applicant eligible or ineligible to be considered for the loan. 

For example, the lender may include the borrower’s debt-to-income ratio, properties and assets at market value, and income streams to provide an overview of whether the borrower is a good candidate for the loan. 

Some lenders take extra steps to double-check the applicant’s claims. 

For example, lenders might hire a valuer or research property rates to verify your real estate investment’s current market value. The valuer’s report is added to the packet for the underwriter’s reference. 


Many borrowers, especially those with an excellent credit record, browse their options before accepting a lender’s offer. 

The borrower might want to negotiate a lower rate or ask for a fixed rate instead of a floating rate. 

Term Sheet Disclosure 

A term sheet is a summary of the loan. It’s a non-binding document that contains the terms and conditions of the mortgage deal. 

The term sheet includes the tenure, interest rate, principal amount, foreclosure charges, processing fees, and other relevant details. 

Loan Closing 

If the negotiations go well and the borrower accepts the offer, the lender closes the loan. 

The lender creates various closing documents, including final closing disclosure, titling documents, and a promissory note. 

The borrower and lender sign the documents, and the lender disburses the funds as agreed. 

3 Ways to Automate Loan Origination 

Now that you know the loan origination process, let’s talk about automating parts of this process to make it more efficient. 

Digitizing Loan Applications  

You can create an online portal where applicants can initiate a loan application and upload their KYC and other documents. 

The documents are automatically transferred to your internal systems for prequalifying the applicant. 

IDP extracts and relays the applicant’s data from the documents to your system. 

Once the data is in the system, robotic process automation (RPA) can be used to determine if an applicant should be prequalified. 

You can use a machine learning (ML) algorithm for deeper insights. 

ML can help identify characteristics that make a person more or less likely to service the loan until the end of the term, allowing you to make smarter decisions.

Assembling Loan Documentation 

Cloud-based RPA can collect documents from the online portal and organize them in one location. Not only are digital copies faster to collect, but they’re easy to store and search. 

Think about it. You’ve received an application, but it’s missing the cash flow statement for last year. You’ll need to email or call the applicant to upload the documents, wasting your and the applicant’s time. 

Lenders typically have a document checklist. Automating checklists is easy with RPA — when the applicant forgets to upload a document, the RPA can trigger notifications to the applicant. 

The system can also notify the loan officer if the applicant becomes non-responsive. 

Speeding Up Underwriting 

Borrowers want faster access to funds, but lenders must complete their due diligence. 

Lending automation can help reduce the time between application and approval. 

With technologies like artificial intelligence (AI), ML, and RPA, automation systems can assess an applicant’s creditworthiness within seconds. 

RPA can help with basics like checking the minimum credit score and income levels. RPA can also flag any areas that indicate greater risk, such as excessive variability in the applicant’s cash flows. 

Moreover, loan origination is a compliance-heavy process. It’s easy to forget a small step when you’re overwhelmed with loan applications. 

RPA ensures all compliance steps are taken care of. If they’re not, the system can trigger alerts for the underwriter as well as the superiors. 

AI and ML can provide deeper insights. These technologies can use big data to identify patterns that make a borrower more likely to default, allowing you to make smarter decisions fast. 

Moreover, AI and ML can help you look beyond credit scores and find people that are more creditworthy than their credit scores suggest. 

As Dimuthu Ratnadiwakara, assistant professor of finance at Louisiana State University, explains: 

“Traditional models tend to lock anyone with a low credit score—including many young people, college-educated people, low-income people, Black and Hispanic people and anyone who lives in an area where there are more minorities, renters and foreign-born—out of the credit market.” 

How Shortening Loan Origination With Automation Helps 

Shortening the loan origination process benefits you in multiple ways: 

  • Match customer expectations: You’ll deliver on the customers’ expectations by offering faster loan processing. 40% of mortgage customers are willing to complete the entire process using self-serve digital tools, but 67% still interact with a human representative via phone. 
  • Deliver better experiences: Automation offers various opportunities to improve customer experience. For example, you can set up an AI chatbot that responds to customers’ questions in real time. 
  • Increase efficiency: You can process more applications per month by automating loan origination. 
  • Better use of your staff’s time: Automated workflows allow credit officers to focus on parts of the business that require a human touch, such as building stronger relationships with clients, than on repetitive tasks that automation can perform more accurately. 

Loan Origination Automation with Blanc Labs 

Selecting the right partner to set up loan origination automation is critical. Partnering with Blanc Labs ensures frictionless implementation of a personalized loan origination system. 

Blanc Labs tailor-makes solutions best suited for your needs and workflow. Blanc Labs starts by assessing your needs and creating a strategy to streamline your loan origination processes. Then, Blanc Labs creates an automation system using technologies like RPA, AI, and ML. 

Book a demo to learn how Blanc Labs can help you automate your loan origination workflow. 


The Benefits of an Automated Loan Origination System

The benefits of an automated loan origination system collectively add immense value for you and your customers. 

Deloitte and the Institute of Management Accountants (IMA) surveyed finance professionals across the globe and found that respondents plan to implement or are already implementing robotic process automation (RPA) (22.8%) and artificial intelligence (AI)/machine learning (ML) (21.4%). 

Why should your organization be left behind? 

In this article, we explain the benefits of an automated loan origination system and help you understand how exactly it can impact your loan origination process. 

Automated Loan Origination Vs. Manual Loan Origination 

Manual loan origination is a loan origination system where you handle the customer’s application manually, from processing documents to assessing the customer’s credit risk profile. 

On the other hand, an automated loan origination system allows you to automate the end-to-end loan process, from application to documentation, underwriting, and administration. 

Automated loan origination systems typically use a combination of technologies like intelligent document processing (IDP), RPA, AI, and ML. 

Automating the loan origination process solves two critical challenges for lenders: 

  • Keeping the turnaround time under control 
  • Finding specialists who can reliably complete the pre-underwriting process

An automated loan origination system involves using software solutions. Your team will likely experience a learning curve, and that’s why you should work with a company that can ensure the learning curve is gentle. Blanc Labs partners with UiPath to provide automation solutions—learn how Blanc Labs hyper automated a client’s loan origination process. 

Benefits of Automated Loan Origination Before and After

The Benefits of Automation the Loan Origination 

Like with any other investment, you’re probably asking what you stand to gain after investing in an automated loan origination system. Let’s talk about the most prominent benefits of automating loan origination. 

Improved Productivity 

According to a McKinsey study, automating the KYC process can increase the number of cases processed by 48%. Automation also minimizes the risk of error, further improving productivity. 

Solutions powered by AI and ML can fast-track loan processing, allowing your team to process more applications per month. Your staff won’t have to handle paperwork manually or spend hours hunting for minor details in the customers’ documents. 

This allows your team to dedicate more time to other revenue-generating activities and exceeding customer expectations instead of mundane tasks. 

Think about the KYC (Know Your Customer) process as an example. You could have a team member collect KYC documents, verify them, and store them for future reference. Or you could use IDP to automate all these steps, allowing your team to focus on sales and support. 

Once you receive a digital copy of KYC documents from a loan applicant, you can use IDP to input the data into your system — with zero manual effort. 

An Accenture case study explains the impact of automation on one of their banking clients. The case study describes how Accenture helped them create new automated processes that improved productivity by over 40%. 

Accurate Decision Making 

The best automated loan original systems use your strategy and data in the system to make accurate decisions. Machine learning algorithms can even provide deeper insights for decision-making by eliminating data silos. 

For example, you might have applicants you can’t score based on traditional credit models. Machine learning models can help assess the risk of such applicants, allowing you to achieve financial inclusion objectives without breaching your risk threshold. 

According to a report by McKinsey, you can automate various parts of credit decisioning or make better decisions based on the data collected by the automated loan origination system. Here’s how an automated loan origination system can help improve credit decisioning: 

  • Credit qualification: Using automated systems to qualify a largely unbanked or underbanked segment by analyzing thousands of data points that were previously inaccessible (such as data from social media and browser history). 
  • Limit assessment: Determining the maximum amount to lend a customer by assessing data from financial statements, tax returns, and other documents using optical character recognition (OCR) as well as new data sources like email and e-commerce expenditure (with the customer’s permission). 
  • Pricing optimization: Analyzing an applicant’s risks with more data instead of simply relying on traditional credit scoring models allows for determining a more accurate rate of interest while keeping risk costs low. For example, ML models can use natural language processing (NLP) to draw insights from a customer’s interaction with the sales reps or determine a customer’s propensity to buy based on the types of financial products. 
  • Fraud management: Detecting fraud is critical when loan origination processes move fast. There are various ways to detect fraud effectively using AI. For example, you might use image-analytics models to interpret a person’s expressions before the brain has a chance to control them. 


You can create rules based on these insights to automate one or more parts of the loan origination process, and you can also change these rules over time. For example, the World Bank uses a Decision Authority Matrix to automatically leave low-risk decisions to RPA, while some critical decisions require the involvement of the President. 

Well-Defined Workflows 

Automated systems help create well-defined workflows. The system gives you more control and visibility over the process, allowing you to regularize processes like storing documents, manual approvals, and assessment of an applicant’s risk. The lending automation system relies on the information already in the system and trigger-based actions to accomplish this. 

With a well-defined workflow, the next step is always clear. But in some cases, there may be caveats. For example, the customer might have requested a loan for an amount larger than your brand can approve. You need to communicate this to the customer along with the amount your institution can lend. 

The best automated loan origination systems can use rule-based decision-making to automatically trigger a notification to the customer, informing them about the maximum amount that can be lent by your branch. 

You can also address other caveats using the rules-based mechanism. For example, you can configure the system to make complex calculations based on the underwriters’ criteria for a consistent approval workflow. 

Enhanced Scalability 

Scalability is one of the most significant bottlenecks for any lender that uses manual processes. Here are the problems with manual loan origination: 

  • Hiring fast enough to meet a sudden spike in demand is tough. 
  • Scaling back during an economic downturn can be challenging. 


The Origination Insight Report by ICE Mortgage Technology (a leading cloud-based provider for lenders) reported that, on average, lenders take about 50 days to close a loan. 

Lenders can overcome this bottleneck with automation. You can automate various parts of the process to increase scalability, such as: 

  • Collecting pre-qualification data: A team member might spend hours manually identifying pre-qualified candidates, but automating can earn all of that time back for your team member. Assessing forms using IDP and AI can help automatically select pre-qualified customers at scale. 
  • Streamline information management: Paperwork, whether physical or digital, can get messy in large volumes. Automation solutions can help you and your customers fetch the correct information quickly without shuffling through a stack of papers. This allows you to work faster and process more applications each month. 
  • Data extraction: You can extract data from physical and digital documents using IDP, AI, and NLP. These technologies can transmit the extracted data to business applications like a customer relationship management (CRM) or enterprise resource planning (ERP) system. Whether you’ve approved 100 applications or 1,000, you won’t have to spend a single minute migrating data manually when you use these technologies. 

Insights for Continuous Improvement 

An automated system collects a large volume of internal data while performing repetitive tasks. The same system can put this internal data in perspective by drawing insights from big data. 

An automated system can offer four types of analytics: 

  • Descriptive analytics: Involves looking at historical data to identify trends. This type of analytics helps you learn more about your customers — what customer traits influence loan performance? When lending to high-risk borrowers, which loan structure and tenure minimize the risk of default? 
  • Diagnostic analytics: Helps understand why something happened based on historical data. 
  • Predictive analytics: The system’s algorithm predicts what will happen based on data. 
  • Prescriptive analytics: Involves using AI, ML, and structured and unstructured data to answer how you can make something happen. Example: you can determine the geographic locations where you’re most likely to see success for a specific type of loan product. 

The best automated systems auto-generate reports and summaries, so you can quickly go to your dashboard and get key metrics for your loan origination process. 

Better Customer Experience 

Digital originators are quickly gaining market share and giving banks a run for their money. The reason? They match a modern customer’s need for convenience, speed, and transparency. 

The faster you process applications, the better your customers’ experience. The problem, though, is that the loan origination workflow is often extensive. You need to assess the applicant’s credit profile and collateral and check all the regulatory boxes. 

An automated loan origination system can do much of the heavy lifting for you. It can help make decisions based on the customers’ credit profiles, verify the validity of the collateral, as well as ensure compliance. 

You’ll be able to focus on delivering improved customer experiences by minimizing processing times and using your own time to focus on value-adding tasks. As McKinsey explains in one of its articles: 

“Instead of processing transactions or compiling data, [the operations staff] will use technology to advise clients on the best financial options and products, do creative problem solving, and develop new products and services to enhance the customer experience.” 

Automate Loan Origination with Blanc Labs 

Selecting the right partner to implement automated loan origination helps ensure you have the support you need to address the complexities of lending automation. That’s where Blanc Labs comes in. 

Blanc Labs can create a personalized automation system for your organization. The three steps Blanc Labs uses to help lenders include assessing your needs, streamlining your loan origination processes and related platforms, and creating an automation system using technologies like IDP, AI, and ML. 

Book a consultation or discovery session with Blanc Labs to learn how we can help automate your end-to-end loan origination process. 


10 Tips to Successfully Implement RPA in Finance

Automation has taken the finance industry by storm, and for all good reasons. Banking automation technologies like Robotic Process Automation (RPA) come with the promise of streamlining processes and increasing efficiency.  

According to Forbes, RPA has the potential to transform the way finance functions, from reducing manual errors to freeing up valuable resources. To help you maximize the benefits of RPA in finance, we’ve put together a list of 10 tips for successful implementation. But first, let’s take a step back and explore what RPA is.  

What is Robotic Process Automation (RPA)? 

RPA is the use of software robots to automate routine and repetitive tasks like document processing, freeing up employees to focus on strategic activities that can lead to better customer service or innovations that could meet customer expectations.  

The bots are programmed to follow specific rules and procedures to complete a task, just like an employee. They can interact with various software applications and systems, such as spreadsheets and databases, to collect and process data. The bots can also make decisions, trigger responses, and communicate with other systems and software.  

When a task is triggered, the bot performs it automatically, eradicating the scope of errors that may be produced through manual processes. The process is monitored and managed by a central control system, allowing adjustments and updates to be made as needed. 

Think of RPA as a digital workforce, working tirelessly in the background to complete tasks that would otherwise take hours to complete. With RPA in finance, your financial institution can increase efficiency, reduce costs, and improve the accuracy of its processes.  

The Use of RPA in Banking Automation 

There are many ways in which RPA can be used in banking automation. Here are some of them: 

Customer Service Automation 

RPA is capable of automating routine customer service tasks such as account opening, balance inquiries, and transaction processing, allowing bank employees to focus on more complex customer needs. 

Loan Processing 

RPA in banking and finance can streamline the loan processing workflow by automating repetitive tasks such as document collection and verification, credit score analysis, and loan decision-making. 

Fraud Detection and Prevention 

In 2022 alone, banks and financial institutions lost $500K to fraud. Technologies like RPA can analyze vast amounts of data to detect and prevent fraud in real-time, improving the accuracy and speed of fraud detection. It may also report any suspected fraud to the banking authorities as soon as it is discovered. 

KYC and AML Compliance 

Verification and compliance document processing can eat up a major share of your institution’s resources. RPA can automate the process of collecting, verifying, and analyzing customer information, helping banks to comply with KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations. 

Back Office Operations 

RPA can automate various back-office tasks such as data entry, reconciliation, report generation, monthly closing, and management reports, freeing up employees to focus on more strategic initiatives. 

Payment Processing 

Manual data entry in payment processing can lead to manual errors and longer processing time. RPA can automate the payment processing workflow, including payment initiation, authorization, and settlement, reducing the risk of errors and improving efficiency. 

Risk Management 

Banks and financial institutions are constantly at risk from various sources. RPA in finance can help institutions identify, assess, and manage risks by automating data collection, analysis, and reporting, improving the accuracy and speed of risk assessments. 

Internal Compliance Monitoring 

RPA can automate the process of monitoring and reporting on compliance with internal policies and regulations, reducing the risk of non-compliance and improving overall compliance management. 

 The potential of RPA in banking and finance is unlimited. When combined with Intelligent Automation technologies, RPA can leverage Artificial Intelligence (AI) and Machine Learning (ML) to provide more intelligent process automation. While the technology in itself is efficient, financial institutions must know how to implement it for maximized benefit.  

10 Tips for Successfully Implementing RPA in Finance 

According to the Deloitte Global RPA survey, 53% of organizations who took part in the survey have already begun their RPA implementation. The number is expected to rise to 72% over the next year. Entering the RPA race can be quick, but managing and scaling it is a different ball game. Before getting started with automation initiatives, it is important to consider the following tips.  

Start Small 

While RPA might seem useful to rejuvenate all of your systems and processing, it is important to consider the business impact and start small. Beginning with a smaller project, for example, a single process or department can help build momentum and demonstrate the benefits of RPA. It also allows the organization to gain experience and develop a better understanding of the technology before scaling up. 

Define Clear Goals 

The key to successful RPA implementation rests in your goals. Decide what you want to achieve with RPA in consultation with your IT department. Having a clear understanding of the goals and objectives of the RPA implementation will ensure that resources are allocated appropriately and that the project stays on track. Aim for specific, measurable, achievable, relevant, and time-bound (SMART) goals.  

Involve Stakeholders 

Engaging stakeholders, such as the management, finance employees, and IT, can help build better design and smoother change management for the RPA implementation. It also ensures that the RPA system is well-integrated across departments and addresses the needs and concerns of all stakeholders. 

Assess Processes 

When a financial institution leverages too many bots to automate processes, it results in a pile of data. They may be tempted to use ML on the data and create a chatbot to make customer queries easier. However, it can lead to  a poorly planned ML project. Thus, a thorough assessment of the processes is critical to ensure that the RPA implementation does not get sidetracked. This assessment should include an analysis of the tasks, inputs, outputs, and stakeholders involved in the process. 


10 tips to implement RPA in Finance_infographic_Blanc Labs

Choose the Right Tools 

Selecting the right RPA tools and technology is critical to the success of the implementation. Decide the mix of automation technologies your institution requires before reaching out to a vendor. Factors to consider include the cost, scalability, and ease of use , as well as its ability to integrate with other systems and applications. 

Define Roles and Responsibilities 

Clearly defining the roles and responsibilities of the RPA team, including project governance, testers, and users, is important to ensure that everyone knows what is expected of them. Remember that automation is a gradual process, and your employees will still need to interfere if it is not properly automated.  

Ensure Data Security 

Protecting sensitive data and customer information is a key concern in finance. RPA needs to be implemented in such a way that data security remains unaffected. Discuss with your vendor about strong security measures to ensure that data is protected and that the confidentiality of customer information is maintained. 

Plan for Scalability 

There are thousands of processes in banks and financial institutions that can use automation. Thus, the RPA implementation should be planned with scalability in mind so that the technology and processes can be scaled up as needed. This helps to ensure that the RPA can be combined with AI and ML technologies as necessary and the implementation remains relevant in the long term. 

Monitor and Evaluate Performance 

Continuously monitoring and evaluating the performance of the RPA bots is critical to ensure that they are operating effectively and efficiently. Also, do not forget to keep HR in the loop so that employees are informed and trained about the changing processes and how to use them in a timely manner. Regular evaluations should be conducted to identify areas for improvement and to make adjustments as needed. 

Foster a Culture of Innovation 

Encouraging a culture of innovation and experimentation can help to ensure that the organization is prepared for the future. Consult your IT department and automate your entire development lifecycle to protect your bots from disappearing after a major update. Invest in a center of excellence that can create business cases, measure ROI and cost optimization and track progress against goals.   

Implementation Automation with Blanc Labs 

At Blanc Labs, we understand that every financial and banking institution has unique needs and challenges when it comes to RPA implementation. That’s why we offer tailor-made RPA solutions to ensure seamless implementation for our clients.  

Our RPA systems are designed to be flexible and scalable, allowing our clients to start small and grow as needed. We provide end-to-end support, from process assessment and design to implementation and ongoing assistance, to ensure that our clients realize the full benefits of RPA. 

Booking a free consultation with us is the first step toward successful RPA implementation. Our team of experts work closely with each client to understand their specific requirements and goals to build a customized RPA solution to meet their needs. 

Interested in hearing how we can accelerate your digital transformation?