Articles

Healthcare Interoperability: Challenges and Benefits

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.

Articles

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:

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

Articles

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.

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How to Automate Loan Origination Systems

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

Translation?

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

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.

Negotiation

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.