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The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise

Insight | AI | Enterprise Tech | Generative AI

The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise

November 27, 2023
Image of a garden

At this point, enterprise generative AI applications for  software developmentmarketingcustomer/employee service , and product design are proliferating as the standout use-cases early adopters are focusing on. From our position working with clients to develop and enable enterprise data + AI solutions, we’ve gained perspective on several of the foundational characteristics enterprises will need to develop to realize on the promise of this 3rd wave of the human-computer revolution.

Even though it is still early days we are very enthusiastic about the prospects of LLMs and powerful ML capabilities to unlock vast economic value and growth for progressive enterprises. Some are estimating an impact of $1 trillion, or 4% of GDP, in the U.S. alone and the generative AI market is expected to see rapid growth, rising from 11.3 billion USD in 2023 to 51.8 billion by 2028.

But First, a Disclaimer

Blanc Labs focuses on the financial services and healthcare industries. We see both sectors as having massive potential in terms of the benefits offered through the application of these transformative technologies.  Both healthcare and financial services are hugely complex, highly regulated, and depending on who you speak to, resistant to change. We believe the stage is set for a broader divergence of winners and losers: organizations that build capability and competency in adopting new tools and leverage the power of their enterprise data will stand to reap the benefits of their investments.

So with all the hype surrounding generative AI and the rapid advancement of a field that feels to moving forward at the speed of light, it is important to understand why some organizations will be better suited to create value in this field than others, and what organizations can do to ensure success.

⛔️ Cloud Infrastructure Only ⛔️

AWS Enterprise Strategist and Evangelist, Phil Lebrun is a pretty smart guy. In one of his posts about the topic, his flagship advice is “Don’t try this without the cloud.” You want your teams focused on problem-solving and innovation, not on managing the underlying complexity and cost of enabling infrastructure and licenses. The cloud is the enabler for adopting AI, making available cost-effective data lakes, sustainably provisioned GPUs and compute, high-speed networking, and consumption-based costing.

Enterprise Platforms are a Lynchpin

If we consider that having completed a cloud migration or cloud native approach is table stakes for AI  enablement within an organization, the next big question will be where your enterprise data resides. The organization’s technology infrastructure and enterprise platform ecosystem will in many ways determine what toolsets may be available to facilitate experimentation and development of AI enabled use cases.

If you are a CIO, CTO or CDO, start by taking stock of the platforms that house your enterprise data and evaluate the toolsets offered by these power technology-ecosystem juggernauts. Microsoft and AWS are emerging as clear leaders in terms of providing their customers powerful tools to apply ML and AI to their enterprise data, but Salesforce, SAP, Pega and OpenText – amongst many others will offer tooling to access, manage and apply AI to enterprise data.

It is impossible to predict who the big winners and losers of this space will be, partially because they are such massive and well entrenched players but keep an eye on this space over the next few years.

The 👿 is in the Data [Taxonomy]

Building an enterprise data model across a complicated operating environment like a bank is no small feat. Standardizing data definitions between different lines of business in a bank involves tackling the complexity of enterprise (+legacy) platforms used to store and handle data. This complexity extends to accommodating the diverse needs of various stakeholder groups with distinct preferences for accessing, manipulating, and analyzing data.

In many ways, risk is a unifying theme and responsibility within regulated industries like the banking sector. We see conformance to risk measurement and regulatory reporting requirements as a lightning rod for action amongst FI’s to get their “data house” in order but proactive approaches amongst banking and technology executives are few and far between.

In a recent interview with Tech Exec magazine, VP of Data and Adaptive Intelligence at Munich Re Canada Branch (Life), Lovell Hodge encapsulated the shift in how progressive organizations are thinking about their data:

“Over the years, there has been a realization that data has an inherent information value to the organization. As the years go by, we’re rapidly producing more data at an increasingly and somewhat alarming rate. So, what a lot of folks in the industry have realized is that there is not a huge space between theoretical concepts around information from data, and how they can use that information to simplify internal processes and also benefit clients.
So, you’re seeing that interdependence starting to mature. And because of that, many organizations are looking at their data not as just something they have to store, but as an asset they can leverage. And once you start thinking in that sort of way, then you start to formulate methods by which you can gain value from data, and it becomes an important part of your corporate strategy.”

App Design and Development Now Occurs ‘In the Data’

Historically, software design and development occurred in a sandbox that was devoid of enterprise data. In traditional dev environments we piped in dummy data to test parameters and during QA tested connectivity and security. But with the explosion of sophisticated ML and AI tools, application development will very much occur at the nexus of querying, manipulating, analyzing and visualizing proprietary data sets.

There is a tremendous amount of opportunity for these new tools to democratize data for business users through the use of conversational/natural language query tools to conduct analysis and derive insights from enterprise data sets.

Another big change will be the blurring of the lines between the role of developers and data scientists and in many cases, these resources will be working directly alongside each other.  Technical resources will require access to live data sets to facilitate model training and to leverage powerful data analytics toolsets. This is going to require a significant shift (from reactive to proactive) in terms of access management and data security.

Firms that work with external partners will require them to adhere to an extremely high standard of data security protocols i.e., ISO and SOC 2 compliance certification with regular audits.

Specific sector, domain, and knowledge of business context has always counted for a lot but increasingly developers won’t be able to rely on detailed functional, technical and design requirements documentation to define the specs of what they are developing.  It will require a much more experimental, test and learn approach.  Toolsets are only going to get more user friendly to work with but developing high impact apps leveraging advanced AI/ML techniques will require a detailed and nuanced understanding of user needs, workflow, data inputs, command prompts, prompt modelling strategies, and a continuous improvement approach to get to a place of lasting and tangible impact for businesses and teams.

In Summary:

There is much still to be learned through this large-scale adoption of a suite of technologies that are evolving at such a rapid pace. This is by no means a complete list of the things business teams and leaders need to consider as they look to capture competitive advantage from these powerful new tools. At Blanc Labs, we are excited about the future and look forward to working with companies to develop their capabilities to create business value by harnessing the tremendous potential of their enterprise data.

About the Author

David Offierski
VP Partnerships & Marketing

With over 15 years in technology consulting services, I am thrilled at how next-gen technologies will unlock a new wave of innovation and productivity for enterprises.

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

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

July 11, 2023
Digital Transformation

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.

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.

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.

Blanc Labs Achieves SOC 2 Type 2 Certification

Technology | Security

Blanc Labs Achieves SOC 2 Type 2 Certification

June 2, 2023
Blanc Labs in SOC 2 Type 2

Blanc Labs, a leading provider of enterprise-level professional services, is proud to announce that it has successfully obtained the SOC 2 Type 2 certification. This certification validates Blanc Labs’ commitment to securely managing data and protecting the interests of its clients, cementing its position as a trusted partner for security-conscious businesses.

SOC 2, developed by the American Institute of CPAs (AICPA), is an auditing procedure that defines rigorous criteria for managing customer data based on five essential “trust service principles”, namely: security, availability, processing integrity, confidentiality, and privacy. The Type 2 certification signifies that Blanc Labs has not only implemented the necessary controls to meet these principles but has also demonstrated their effectiveness over an extended period.

“At Blanc Labs, we understand the paramount importance of data security and privacy. This certification is a testament to our unwavering commitment to safeguarding our clients’ sensitive information, and it reinforces our dedication to maintaining the highest standards of excellence in all aspects of our operations,” said Hamid Akbari, CEO of Blanc Labs.

Blanc Labs’ comprehensive approach to security extends beyond achieving certifications. The company continually invests in robust security protocols and employee training programs to ensure a holistic and proactive approach to data protection.

As technology continues to evolve, data security and privacy remain critical concerns for businesses. Blanc Labs’ commitment to obtaining and maintaining the SOC 2 Type 2 certification showcases its dedication to meeting the highest standards of security and privacy in an ever-changing landscape.


Benefits of Intelligent Document Processing

Financial Services | Banking Automation | Digital Banking | IDP | Lending Technology

Benefits of Intelligent Document Processing

January 20, 2023
Benefits of IDP

If you are reading this, chances are that you are exploring intelligent document processing (IDP) systems for your business.

Many businesses are curious about this document automation process because, just like you, they may have heard about how it simplifies complex document layouts, captures data and organizes it for a seamless workflow.

In this article, we will explore:

  • What Is Intelligent Document Processing?
  • How Intelligent Document Processing works
  • Benefits of Intelligent Document Processing

What Is Intelligent Document Processing?

Intelligent document processing uses Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), computer vision, and Intelligent Character Recognition (ICR) to automate data extraction from complex semi-structured and unstructured documents. This technology helps you categorize the extract data into a meaningful format that is easier for people or a system to comprehend. A popular use case for intelligent document processing is mortgage origination and decisioning, where applications can run into hundreds of pages.

Read more on how Intelligent Document Processing helps financial services organizations.

How Does Intelligent Document Processing Work?

IDP uses deep-learning AI technology to scan complex data, extract it and organize it into predefined categories. The best part is that this technology can be trained in up to 190 languages.

One of the best ways to understand how document processing works is to look into a few use cases first. Here are multiple ways in which IDP helps organizations from various industries to automate their data.

IDP for Human Resources

One of the popular intelligent document processing use cases is human resources. This paper-intensive industry has already transitioned to data automation by:

  1. Screening resumes and capturing the right skill sets that match a job description. This has helped HR teams avoid going through resumes manually and narrow down on candidates who are fit to take up the role, hassle-free.
  2. Keeping all the employee data in one place. Earlier, HR teams were forced to manage hundreds of thousands of files for new and existing employees. Not only was it hard to find specific data quickly, but it was hard to comprehend at times. But with IDP, it is easier to update and extract the employee data when need be.
  3. Simplifying the employee onboarding process by capturing employee information based on the forms filled in by them.

IDP for Mortgage Processing

Another data heavy use case you can refer to is mortgage processing. Given how vast the industry is, you can only imagine the number of documents that accumulate at every stage of the mortgage process. Let’s see how IDP simplifies document accumulation and data extraction for this industry:

  1. IDP is capable of processing high-volume mortgage data and identifying possible risks one may face during the process. With IDP, mortgage officers can identify the cause for rejection and inform approvers about these potential risks.
  2. Validating documents is time consuming. However, IDP helps you validate the data from documents with a button and offers you insights on whether to move ahead with the mortgage or not.
  3. You can also audit each mortgage application with IDP and that too without human intervention. It can be time consuming to audit documents for a mortgage even when you have a professional helping you out with the process. With IDP, you can save time and also check the authenticity of each document.

Top 7 Benefits of Intelligent Document Processing

  1. Increases Employee Productivity

One of the benefits of using IDP is its ability to increase employee productivity. Intelligent document processing helps employees free up their time that they spend on doing repetitive tasks such as data entry and record management. Lesser time spent on repetitive tasks helps increase their productivity.

  1. Helps Reduce Manual Work

IDP is also known for reducing manual work as it enables extraction of data from a document,  an image, sound, or even a video using its AI technology. What’s more, the data extracted is then transformed into text, thereby helping employees reduce manual work on such tasks. It uses Natural Language Processing (NLP) that understands the content and converts that data into text.

  1. Automates Classification of Documents

Once the data is converted to text, it is classified into different categories, without the need for human intervention. This helps your organization with data collection and streamlining the document management process with minimal errors.

  1. Enables the Processing of Large Volumes of Documents

Another benefit of using IDP is processing large volumes of documents in one go. While humans take time to extract data from each document, the same isn’t true in the case of IDP. Unlike humans, a document processor can extract data from multiple documents simultaneously. You can tackle a giant database and avoid spending capital on a data entry team.

  1. Improves Data Accuracy

Humans are prone to make errors when feeding data to a database, especially when there are a large number of documents. This may hamper the overall authenticity of the database and may leave managers questioning its accuracy.

Thankfully, the same isn’t true in the case of AI and ML-based IDP technology. It is capable of entering the data accurately in the database and retrieving the same data with speed when its users need it on an urgent basis. In short, an automated document processor can extract accurate data and eliminate mistakes with an accuracy of more than 90%.

  1. Increases User and Customer Satisfaction

Intelligent Document Processing is capable of providing users with optimal responsiveness through the life cycle of work to be performed. With IDP, you can process evidentiary documents, extract the right keywords from a data set in less time. What’s more, the automated process can use keywords to route emails to the right department for faster throughput from start to finish. This helps speed up the response time between receiving an origination request to approving it, from a claim initiation to notification of completion, etc. Documents are no longer the bottleneck.

  1. Offers Data Security

When documents are managed manually or in disparate data systems, there is always a possibility of a data breach. But with IDP, documents can be kept in a secure, centralized location. This enables businesses to be more compliant with data protection regulations. In other words, you can make sure that the data saved never gets misused by anyone.

Maximize the Benefits of Intelligent Document Processing

Benefits such as these (and others discussed in the article above), will help you identify various reasons why a data extraction processes like Intelligent Document Processing are essential for your business. Once you decide to opt for intelligent document processing, it’s time to look for the right implementation partner.

Why Choose Blanc Labs’ Intelligent Document Processing?

Blanc Labs believes in driving improved operational efficiency and better business outcomes.

Blanc Labs’s IDP solution can:

  • Automate workflows for document collection, digitization and analysis
  • Replace manual effort through intelligent data capture
  • Connect with third party data providers for analysis and insights
  • Analyze document data, provide status alerts, and flag fraudulent entries
  • Secure documents in a drop box
  • Deploy on premises, in the cloud or as a hybrid model

We hope that learning about these benefits will help you arrive at a decision faster and invest in the best document processing solution in the market.

The Transformative Power of Banking Automation

Financial Services | Customer Experience | Enterprise Automation | IDP | RPA

The Transformative Power of Banking Automation

January 10, 2023
The Transformative Power of Banking Automation
Image credit: vectorjuice on Freepik

McKinsey expects machines to be responsible for up to 10% to 25% of a bank’s functions. The reasons? Banking automation minimizes the need for your team to work on repetitive tasks, allowing them to focus on high-profile and strategic aspects of the business.

Automation also improves accuracy, which can save you a ton of money — a major reason why 80% of finance leaders have implemented or plan to implement Automation (including Robotic Process Automation).

Curious about how banking automation can help? We explain everything you need to know about how automating your banking workflow can help reduce costs and improve efficiency.

What is Banking Automation?

Banking automation involves using software powered by multiple technologies like AI (artificial intelligence) and ML (machine learning) to automate repetitive tasks. Automation has three primary benefits:

  • Frees up your team’s time for more strategic tasks
  • Improves process accuracy
  • Improves the Customer Experience (CX) and the Employee Experience (EX)

For example, you can automate your account opening process. A customer requests a new account via the chatbot on your website. The chatbot provides an application form. The applicant fills out the form, and it’s sent to your RPA robot. The robot performs the basic procedures, including checking the credit score and KYC verification.

Next, the robot scans the applicant’s documents using OCR (optical character recognition) for data extraction. The robot matches the information in the documents and the application form. It flags any details that don’t match and sends them for manual approval.

The robot continues to validate uploaded documents using NLP (natural language processing). It finds key data points in the document’s free text, categorizes them, and uses them in the automated process.

The robot then updates the bank’s backend system to create a new business account, provided the customer’s data meets the bank policy. Once approved, the customer receives an automated welcome email.

Why Banks Need Banking Automation

Banks need automation to compete in the modern banking environment. Now, that’s a broad statement, so here are specific reasons why a modern bank needs automation:

  • Allowing employees to focus on tasks that require a human touch: Most banks were set up long ago. Manual forms and workflows were a foundational pillar for legacy banks, and as a result, employees spend countless hours on things like data entry and account verification. Automation allows employees to “hand over” repetitive tasks to software, freeing up their time for high-profile tasks that require a human touch.
  • Record management: RPA can generate and check expense records for compliance. It auto-logs all transactions and prepares the necessary financial records to get an overview of your business’s financial performance and position.
  • Meeting customer expectations: The need for speed is a key driver of a modern customer’s experience. If you’re taking too long for basic operations like opening a bank account, you’ll lose customers fast. Automation can help speed up your processes and help deliver on your customer’s expectations.
  • Faster customer support: Your customers hate waiting hours to get an answer. Automating your support using RPA helps you respond faster. You can answer customers’ questions at scale using a chatbot. Also, you can use an AI-powered chatbot to answer questions you haven’t added as an FAQ.

These factors make automation more of a necessity than a nice-to-have — you need automation to compete neck-and-neck with other banks.

How Banking Automation Can Transform Your Bank

Transforming your bank’s value network with automation offers many benefits in various business aspects, including finance, legal, and customer experience. Here are the benefits of using RPA in banking:

Banking Automation Leads to Efficiency

You can improve productivity by up to 80%, especially if you identify the most impactful productivity levers. The efficiency improvement is a result of two factors:

  • Low manual effort: Employees have more time available once they hand over repetitive tasks to software. They can do more in the same amount of time, helping you scale your operations.
  • Improved accuracy: Errors are expensive because you spend time and resources on correcting the errors. Fewer errors = improved productivity.

A great example of efficiency is automated document processing. As a banker, you probably spend a good number of hours reading documents and inserting relevant data into your systems, depending on your role at the bank. However, you don’t have to spend all those hours manually entering data if you use intelligent document processing.

Better Customer Experience

An average company takes over 12 hours to respond to customer service requests. That’s a recipe for dissatisfied customers, especially if you’re a financial institution.

Your customers expect their money to be in the hands of a reliable entity, and guess what you communicate when you don’t answer customers for over 12 hours?

Using RPA to automate your customer support helps minimize response times. In most cases, the chatbot can provide real-time answers to the most commonly asked questions.

Speed is also critical for other client-side processes. For example, you want to be as fast as possible in opening accounts, processing personal investment requests, or enabling additional services for an account. Automating these processes (while ensuring accuracy) helps improve customer experience.

Compliance and Risk Reporting

According to Deloitte, the cost of compliance for retail and corporate banks has increased by over 60% since the pre-financial crisis spending levels. Non-compliance is even more expensive, but automation can help lower your spending on compliance.

RPA builds compliance into your processes. Automating compliance ensures you’re always meeting regulatory requirements without requiring teams to spend extra time double-checking for compliance.

Automation also creates an audit trail and automatically generates risk reports that give you added insights. The system can identify and flag suspicious activities so that you can investigate them.

Reduced Costs

It’s easy to see how banking automation using RPA can reduce costs. Reduced administrative load, saving time on repetitive tasks, and speeding up processes all yield dividends.

For example, Radius financial group reduced loan processing costs by 70% by using AI to automate their process.

Banking automation also removes human error, so you’ll spend less on fixing those mistakes.

Without automation, you’d need to invest a large amount of money in building more teams as you scale. However, automation empowers you to scale faster. You can continue investing in training current teams and save on costs you’d incur to accommodate a larger workforce.

Automation and Adaptability

Banking automation helps banks adapt faster to a client’s needs or the business environment.

For example, the increasing popularity of Fintech is one of the most significant concerns for banks. Fintechs are quickly gaining market share at the expense of legacy banks. Customers appreciate how a fintech offers better, faster services.

Fintechs aren’t the only factor banks need to consider, though. Your bank might want to integrate banking solutions with a new partner’s ecosystem to offer additional services like tax consulting. Or your bank needs to process offshore transactions faster, especially when the transaction is subject to jurisdictional restrictions on the amount of transfer allowed.

Adaptability is critical for banks to succeed, and automation can make adapting to changes seamless. Implementing an automation solution will improve your adaptability to changes and allow you to quickly catch up with your modern competitors.

The Bottom Line

Over the past five decades, banking has gone from paper-based to almost entirely digital. Next up? Automation.

Automation makes banking frictionless for both internal and external stakeholders — it’s a win-win. The only problem banks face with automation is the lack of a reliable partner who can guide them through the transformation journey. Book a discovery call with us, and we’ll answer all your banking automation questions.

Top Use Cases of Intelligent Document Processing

Financial Services | Digital Transformation | Enterprise Automation | IDP | Lending Technology

Top Use Cases of Intelligent Document Processing

November 22, 2022

Currently, most enterprises have a workflow rampant with manual document-heavy processing.

However, businesses are quickly digitizing their document-processing workflows. 50% of B2B invoices across the globe will be processed without manual intervention according to a Gartner study. The reason? Manual document processing is more expensive than the cost of the documents themselves.

For example, the average cost of processing a single invoice was $10.89 in 2021. Manual document processing is also prone to human errors like fat finger errors. In a world where 90% of the data is unstructured, you need a tool that can automatically convert unstructured data into structured data to supercharge your productivity.

This guide explains how you can use intelligent document processing to save your business plenty of money, time, and resources.

What is Intelligent Document Processing?

Intelligent Document Processing (IDP) is a technology that automatically extracts unstructured data from multiple document sources, including images, online forms, and PDFs. IDP is also known as Cognitive Document Processing (CDP).

IDP converts this unstructured data into structured data using multiple technologies, including natural language processing (NLP), machine learning (ML), optical character recognition (OCR), and intelligent character recognition (ICR). Together, these technologies make IDP intelligent.

OCR is often used interchangeably with IDP. However, that’s not true. IDP uses OCR as one of the technologies to extract data.

How Does Intelligent Document Processing Work?

Here’s how a document is processed using IDP:

  • Conversion: An IDP platform starts by capturing your document through a scanning device. Once it converts a physical document into a digital one, it starts ingesting data.
  • Document image processing: The document’s image is processed for optimal OCR and archival.
  • Reading text using OCR: OCR helps the machine accurately read the scanned document’s text.
  • Identify language elements with NLP: IDP platforms use NLP to find language elements using methods like feature-based tagging and sentiment analysis.
  • Machine learning algorithm classifies information: A combination of machine learning and other techniques is used to classify the information in the document.
  • Extracting elements using AI: IDP uses AI to extract information elements like contact numbers, addresses, and names.
  • Validation: IDP platforms validate information using third-party databases and lexicons for data validation. Data points are flagged when the platform can’t validate them so someone from the team can review them manually.

Top Intelligent Data Processing Use Cases in Banking

Reading and writing financial documents make up a large portion of a bank’s workflow. As a bank, you need to process data fast to offer best-in-class services to your customers without making errors.

IDP helps banks guarantee accuracy and efficiency to their clients. In addition to data extraction’s key role in a bank’s workflow, banks can also use IDP platforms for fraud detection.

Here are some of the most common use cases of IDP for banks.

Mortgage Underwriting

Customer satisfaction with mortgage originators reduced by five points on a 1,000-point scale in 2021 according to a study by J.D. Power driven by record mortgage origination volume. Banks need to automate their mortgage workflow to scale as the demand grows. After all, customer satisfaction is one of the most significant differentiators in the mortgage industry.

The mortgage workflow involves collecting various documents. Extracting data from these documents is one of the major factors slowing down the workflow. This is where an IDP tool can help streamline your mortgage workflow.

An IDP tool helps you speed up the underwriting process with automation. It automatically reads and extracts relevant data and relays it to your bank’s credit evaluation system.

Claims Processing

The P&C Customer Satisfaction Survey reveals that the claim filing process is the biggest driver of customer satisfaction.

However, the claims processing workflow can be complex. Claims data comes in various formats—customers might send data as word files, PDFs, and images. Plus, you might receive the data via multiple channels—you might receive it via email, chat, or over a call.

Unifying this data without manual effort is a massive challenge. Traditionally, banks used OCR to process physical documents. However, the lack of accuracy required manual review.

An IDP tool is a great alternative to OCR for claims processing. Thanks to technologies like NLP, computer vision, and deep learning, it provides greater accuracy than traditional OCR.

Customer Onboarding

Customer onboarding is one of the most resource-intensive processes for a bank. Banks spend an average of $280 to onboard a single client according to Backbase—the cost can add up when you’re onboarding hundreds or thousands of customers every month.

Many of these expenses go towards processing documents, including the bank’s forms, credit reports, or tax returns. Sure, you can try automating this workflow. However, the automation will break down as soon as a new document type is introduced or you change your form’s template.

An IDP tool can help tame your customer onboarding costs. Your customers will appreciate a fast onboarding experience, and you’ll save money, increase productivity, and make an excellent first impression.

Financial Document Analysis

Banks handle thousands of financial documents every day. From financial statements to tax returns, carefully studying financial documents is critical to a bank’s operations.

Financial analysis is a cognitively heavy task. Why make your team spend time on mundane tasks like manipulating data when you can use an IDP tool to automate this process and enable your team to concentrate on their more complex deliverables.

Using an IDP tool helps analysts automatically structure and populate relevant financial data into their system. You’ll do your analyst team a favor by eliminating a lot of their manual work, allowing them to focus on analysis.

KYC Process Automation

KYC (Know Your Customer), Re-KYC, and C-KYC are critical for compliance. Banks might need to refer to a customer’s KYC details at various stages during a customer’s journey.

However, handling hand-written KYC forms is a hassle. Migrating a customer’s KYC data comes with challenges like human error and work overload. Committing errors when underwriting a mortgage or onboarding a customer costs money, but failing to comply with KYC requirements may increase the legal, compliance and regulatory risks.

Using IDP ensures accuracy, so you never have to lose your reputation and pay a fine for failing to comply with KYC norms.  The McKinsey KYC Benchmark Survey found that by increasing end-to-end KYC-process automation by 20%, an organization could enjoy the following positive outcomes:

  • Increased quality assurance by 13%
  • Improved customer experience (by reducing customer outreach frequency) by 18%
  • Increased the number of cases processed per month by 48%

The Bottom Line

Banks process a colossal amountnumber of documents and data each day. Getting new customer data into the system, processing claims, and analyzing financial statements are heavily data-driven tasks that involve dozens of documents from hundreds of customers.

The probability of committing errors is high. Banks also need a large team just to process documents and structure the data in those documents.

Banks need an IDP tool to automate this process and remove the risk of error from the process. It also integrates with applications to make migrating the data easier. An IDP tool also validates data and alerts team members in exception cases, when it requires a human to review accuracy.

It is important to select an IDP tool that offers the right solutions for your industry. Better yet, find a partner who can create a custom IDP solution tailor-made for you.

Why Choose Blanc Labs Intelligent Document Processing?

Blanc Labs partners with financial organizations like banks, credit unions, and fintechs to automate operations.

We can help you create robust automation solutions that minimize manual effort, reduce errors, and improve productivity. Our team helps you use the most advanced technologies including AI and ML to automate complex, resource-heavy processes like document processing.

Book a discovery call with us if your financial organization deals with plenty of documents daily. We’ll come up with a tailor-made solution to minimize the friction in your document processing workflow.

Finding the right API Management Platform

Financial Services | API Management | Digital Banking | IT Management

Finding the right API Management Platform

November 3, 2022
API management

APIs are an integral part of today’s digital world. They are used for secure data exchange, integration, and content syndication. As APIs become more ubiquitous in enterprise businesses, it becomes necessary to manage them efficiently.

Banking and Payments ecosystems are converging with Open Banking and Finance. Whether regulatory or market driven, these digital interactions are happening already – and growing exponentially. Doing APIs and API Management right are central to the growing interdependence and interoperability between Fintechs, Banks and Consumers. Stakeholders are demanding secure access to financial data to drive better customer experiences. A key enabler to that end are the systems that surround APIs.

What is API Lifecycle Management?

API Lifecycle Management is the process of building, controlling, distributing, analyzing, and reusing APIs. It also can include capabilities around intelligent discovery; one pane of glass visible across multiple API gateways and API management systems; bringing to life the visionary end state of monetizing and marketing all these capabilities to external parties to operationalize the concept of “API as a product”. Thus, there are many API Management solutions in the market offering a variety of features. But at the very minimum, an API Manager should allow users to do the following:

Discover APIs

Before you can more effectively govern their lifecycle, you need a simple and configurable tool to find, filter and tag all your API assets into a centralized repository. Simplify complexity and/or get better visibility and facts to position your organization to “open itself up” to the new business realities and opportunities emerging.

Design, build, and Test APIs

The API Management tool should provide everyone, from developers to partners, the ability to create APIs under a unified catalog and test their performance.

Deploy APIs

API Management tools should also allow you to publish APIs on-premises, on the cloud or in a hybrid environment. Additionally, the API Manager may give you a choice between managing the API infrastructure in the tool itself or on your own.

Secure APIs

By providing a central point of control, most API Management tools will ensure that you have full visibility of all your APIs across environments so you can mitigate any vulnerabilities.

Manage APIs

API Management tools should give you a central plane of visibility into APIs, events, and microservices. Most API management tools will allow you to govern APIs across all environments (on-premise, hybrid, cloud) and also allow you to integrate with other infrastructures, including AWS, Azure, and Mulesoft. A good API management tool should also provide multiple predefined policy filters to accelerate policy configuration.

Analyze APIs

An API Manager should give you real time metrics in a unified catalog. By providing data on the business performance or operations across your APIs, you can make better decisions leading to improved business results.

Extend and Reuse APIs

By giving you a single, unified catalog, an API Manager can eliminate duplication and extend the life of APIs through reuse.

The need for API Management

API management centralizes control of your API program—including analytics, access control, monetization, and developer workflows. It provides dependability, flexibility (to adapt to shifting needs), quality, and speed. To achieve these goals, an API Manager should, at the minimum, offer rate limits, access control, and usage policies.

Essential features of an API Manager tool

1. API Gateways

A gateway is the single entry point for all clients and is the most critical aspect of API management. An API gateway handles all the data routing requests and protocol translations between third-party providers (TPP) and the client. Gateways are equally important when securing API connections by deploying authentication and enforcement protocols.

2. Developer Portal

The primary use of the developer portal is to provide a hub, specifically for developers, to access and share API documentation. It is an essential part of streamlining communications between teams. Typically, developer portals are built on content management systems (CMS), allowing developers to explore, read, and test APIs. Other features of a developer portal could include chat forums for the internal and external developer community and FAQs.

3. API Lifecycle Management

As the name suggests, API Lifecycle Management provides an end-to-end view of how to manage APIs. API Lifecycle Management is a means to create a secure ecosystem for building, deploying, testing and monetizing and marketing APIs.

4. Analytics engine

The analytics engine identifies usage patterns, analyzes historical data, and creates tests for API performance to detect integration issues and assist in troubleshooting. The information gathered by the analytics engine can be used by business owners and technology teams to optimize their API offerings and improve them over time.

5. API monetization and marketing

API management tools can provide a framework for pricing and packaging APIs for partners and developers. Monetizing APIs involves generating revenue and keeping the API operational for consumers. Through usage contracts, you can monetize the microservices behind APIs. An API management tool will offer templatized usage contracts based on predefined metrics, including the number of API calls. This empowers innovative external players to help drive your business in ways you have not dreamed up yet – and still do it securely.

How successful is your API management?

Now that we know the features of an ideal API management software, how do you evaluate its success for your API efforts? Here are a few ways to track your progress:

How rapidly can you launch your APIs to meet your business goals? Latency and throughput are ways to measure the speed of deployment. Other areas to measure speed would be onboarding and upgrading APIs.

Flexibility is the breadth of options available to developers when adopting APIs. The greater the flexibility, the higher the cost and effort to manage the API.

How available your APIs are to developers. One way to measure dependability is downtime. Quota is another way to restrict how many API calls can be made by a developer within a certain timeframe. Enforcing quotas makes API management more predictable and protects the API from abuse.

Stable APIs with consistent performance reflect higher quality. It is a way to measure a developer’s satisfaction with the API.

The above four factors contribute to cost. If your API management software provides a better view of all your APIs, it will reduce duplication and costs. Reuse of APIs is another way that you can save costs.

How are you managing API complexity?

If you are a business leader concerned about how to meet market demand through the creation and deployment of APIs, or you would like to monetize and reuse your existing APIs and reduce costs, then you need structured API management.

In partnership with Axway, Blanc Labs offers a way to manage your APIs to bring maximum business value. Axway’s API Management Platform enables enterprises to manage and govern their APIs for developing and applying their digital services.

Book a discovery call with Blanc Labs to learn more. 

Developing the next generation of talent at Blanc Labs’ Digital Academy

Partners | Leadership | STEM | Talent

Developing the next generation of talent at Blanc Labs’ Digital Academy

October 19, 2022
Digital Academy

In August of this year, we launched the Digital Academy at Blanc Labs, where 100 students in Colombia had the opportunity to learn from our experts on topics that we consider key to our business.

The students that joined the program as Associates, gained knowledge of fundamental concepts of agile methodologies, cloud computing, full-stack development, and RPA (Robotics Process Automation).

The Digital Academy community recently shared their top takeaways from this experience.

Promoting a culture of learning at Blanc Labs

“Walking alone through the world of technology is not easy. I am sure many of us have encountered some barriers, such as not understanding what we hear in a video tutorial, or you end up having lots of questions. When we are reading the technical documentation, it may become even more complex. These situations make me appreciate my technology education teachers and the people who share their knowledge with me,” says Gustavo Camargo, a Software Engineering Associate, who is committed to achieving his dream of working in the IT industry.

Martin Bec, who shared his experience as a Full-stack Developer in our latest bootcamp, says: “Teaching and sharing  knowledge is part of my lifelong learning way of living. I encourage others to clear up their doubts and I appreciate the opportunity to learn. My own learning approach significantly affects how I lead others to strengthen their career in IT.”

Working in technology requires a combination of technical skills and soft skills

By being exposed to various virtual collaboration opportunities, students work on their communication, relationship-building, teamwork, and cultural awareness skills. One of their main challenges is to overcome the language barrier, identifying how to strengthen their learning in a second language in their spare time and the IT top skills they require for a booming professional career.

“With agile methodologies, I learned that good planning is key, and I apply that thinking to my personal life. Fulfilling projects requires perseverance, coherence, and the team’s motivation to continue. There may be changes along the way, but the agile methodology is flexible with this, and you learn how to prioritize your work and focus on progress”, says Jennyfer Belalcazar, Systems Engineering Student.

According to a Gartner study published in March 2022, an agile developer must master methods, techniques, behaviors, and various fundamental aspects of Engineering.

  • Methods such as Scrum and Kanban, implementing Agile pilots, pivoting, and adjusting the strategy, and evaluating risks and results for the business, are highly valued
  • Understanding metrics, and User Stories are essential to promote feedback with stakeholders and gain in-depth knowledge to overcome project challenges
  • Customer focus, continuous learning, and collaboration significantly impact interactions and work styles
  • The adoption of best practices, and the importance of test-first thinking, are fundamental for execution. The incorporation of agile architecture and the training of Database Administrators with a set of multidisciplinary skills impacts Agile teams’ performance

As complementary activities to their learning experience, Associates have created sessions to strengthen their conversational skills in English and they engage in activities to improve their personal brand and prepare for job interviews.

As Associates evolve in their learning experience, they gain the most valued skills in this industry. Most students combine their Digital Academy experience with technical education, short courses, and other IT programs, particularly in development, programming, and Data Science.

Building a culture of learning, fostering technology education, and empowering our team is fundamental to the success of our projects.Discover how Blanc Labs can bring an agile and strategic approach to your digital transformation project. Get in touch

Blanc Labs CEO Hamid Akbari Speaks On What It Takes To Scale Up Successfully

Technology | Digital Transformation | Leadership | Podcast

Blanc Labs CEO Hamid Akbari Speaks On What It Takes To Scale Up Successfully

October 7, 2022

You don’t have to be a #Fortune500 company to start thinking strategically about the future of your business. Technology services firms that commit to strategic methods and sustainable growth mechanisms early on can experience exponential profitability.

The way forward is not always clear, but technology services firms can take it one step at a time. Hiring great people, adapting technological breakthroughs, and staying connected with the needs of their customers all contribute to success.

In this podcast hosted by Collective 54, our CEO, Hamid Akbari, talks about what it takes to scale up a company in today’s competitive landscape.

Three Reasons Financial Institutions Are Losing Out to FinTechs

Financial Services | Digital Banking | Digital Transformation | Open Banking | Technology Architecture

Three Reasons Financial Institutions Are Losing Out to FinTechs

June 16, 2022

…And How to Keep Up with Digital Natives 

by Bob Paajanen and Charles Payne

The way we bank has changed forever. While FinTechs have the latest technology innovations, what they don’t have is decades-worth of relationships with customers and large swaths of Big Data. Financial institutions need to recognize this advantage, leverage their data, streamline processes, and thereby empower their relationship managers if they want to compete with their new-age rivals.

Mismanagement of Data

Most financial institutions have multiple customer-facing systems that operate in their own silos. As many as 50% of banks and credit unions state that they have trouble accessing their internal data. Without a single unified view of their customer, banks and credit unions are unable to collect, process, or indeed deploy insights that will enable them to cross-sell products and services to their customers.

The services gap left by financial institutions is especially felt in commercial banking where FinTechs are sweeping up SMBs with targeted products and quicker access to funds. A prominent example of this trend is Shopify, which started out as an e-commerce platform, but is now the tenth-largest provider of financial services to SMBs. Another example is Stripe, which has created an end-to-end lending API (application program interface) as its next offer to SMBs.

Paper-heavy processes, and disparate data management systems are some of the major causes of this issue. As the finance industry moves toward open banking, it is imperative that financial institutions unlock the value of their data and translate it into actionable insights so they can improve their languishing businesses.

why financial institutions are losing out to fintechs
Source: 11:FS ‘Fintech filling services gaps’ Designing digital financial services that work for US SMBs 

Lack of Efficiency

A 2019 Gartner report estimated that process automation, including document processing, could save financial institutions 25,000 hours of avoidable work per year. With advances in technology in the last three years, it is not hard to imagine that this number may have gone up even further.

Most of the productivity loss mentioned above has been attributed to human error. This is hardly surprising when many financial institutions continue to use paper-heavy loan origination models. Without automation, document processing is rife with efficiency and security issues including document mishandling, collaboration on email (generating multiple copies of the same document), versioning issues, loss of time to find the documents when required, a lack of compliance, and a lack of remote access.

Since the pandemic, consumer expectations have changed dramatically. Close to 60% of customers today are open to completing their mortgage applications entirely online without support on the phone or in person. Even more pressing than the platform, is the need for speed, with customer satisfaction falling 15 percentage points for approvals that take longer than 10 days.

With unprecedented demand for mortgages, financial institutions must speed up intake, underwriting and decisioning processes faster if they want to keep the customer’s business.


Inefficient loan origination processes lead to rising costs. On average, loan origination costs $7-9k per application. That is over and above the cost of productivity loss, estimated to be $878,000 (for 25,000 hours lost per year) for a company with a 40-person finance team. This cost is invariably passed on to the customer who may end up paying higher fees and charges compared to what they might pay if they opted for a non-banking entity.

rising cost of loan origination

The FinTechs Are Coming… And how to slow them down

As a result of service gaps and inefficiencies, customers from both commercial and retail banking have been veering towards nonbanking entities for loans. 50% of Canadian SMBs in 2021 felt that they couldn’t maintain their growth strategies “due to a lack of capital.” Today, nonbanking entities, account for more than 70% of total originations. By using automated processes and digital interfaces, non-banking entities or FinTechs are 25% cheaper than the industry average and can deliver a decision 30% faster compared to other financial entities.

In the age of Open Banking, it is important that financial institutions update their legacy processes and unlock the potential of their data if they want to survive.

The automation journey begins with intelligent document processing. Blanc Labs provides a 360-degree IDP (Intelligent Document Processing) solution that can:

  • Automate workflows for document collection, digitization, and analysis
  • Replace manual effort through intelligent data capture
  • Connect with third party data providers for analysis and insights
  • Analyze document data, provide status alerts, and flag fraudulent entries
  • Secure documents in a drop box
  • Deploy on-premises, in the cloud, or as a hybrid model

Book a demo or discovery session with Blanc Labs to learn about the impact of our IDP solutions for banking.