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Understanding the Potential of Generative AI in Healthcare

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

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

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

As a World Economic Forum article aptly explains:

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

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

Generative AI in Healthcare: Potential Use Cases

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

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

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

Drug Discovery

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

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

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

Disease Diagnosis

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

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

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

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

Patient Care and Treatment Plans

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

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

Medical Imaging

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

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

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

Reduce the Friction of Data Access

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

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

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

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

Reduce Physical Burnout

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

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

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

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

Automated Health Assistance

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

Medical Research

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

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

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

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

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

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

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

The Practical Implications of Using Generative AI in Healthcare

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

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

Clinicians Will Spend Less Time on Clinical Documentation

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

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

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

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

AI Will Emulate Medical Decision-Making

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

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

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

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

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

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

Medical Errors Will Reduce Significantly

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

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

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

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

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

The Discovery of New Use Cases Will Continue

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

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

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

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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.

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The Benefits of an Automated Loan Origination System

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

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

Why should your organization be left behind?

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

Automated Loan Origination Vs. Manual Loan Origination

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

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

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

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

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

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

The Benefits of Automation the Loan Origination

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

Improved Productivity

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

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

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

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

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

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

Accurate Decision Making

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

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

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

Credit qualification: Using automated systems to qualify a largely unbanked or underbanked segment by analyzing thousands of data points that were previously inaccessible (such as data from social media and browser history).
Limit assessment: Determining the maximum amount to lend a customer by assessing data from financial statements, tax returns, and other documents using optical character recognition (OCR) as well as new data sources like email and e-commerce expenditure (with the customer’s permission).

Pricing optimization: Analyzing an applicant’s risks with more data instead of simply relying on traditional credit scoring models allows for determining a more accurate rate of interest while keeping risk costs low. For example, ML models can use natural language processing (NLP) to draw insights from a customer’s interaction with the sales reps or determine a customer’s propensity to buy based on the types of financial products.
Fraud management: Detecting fraud is critical when loan origination processes move fast. There are various ways to detect fraud effectively using AI. For example, you might use image-analytics models to interpret a person’s expressions before the brain has a chance to control them.

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

Well-Defined Workflows

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

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

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

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

Enhanced Scalability

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

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

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

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

Collecting pre-qualification data: A team member might spend hours manually identifying pre-qualified candidates, but automating can earn all of that time back for your team member. Assessing forms using IDP and AI can help automatically select pre-qualified customers at scale.

Streamline information management: Paperwork, whether physical or digital, can get messy in large volumes. Automation solutions can help you and your customers fetch the correct information quickly without shuffling through a stack of papers. This allows you to work faster and process more applications each month.
Data extraction: You can extract data from physical and digital documents using IDP, AI, and NLP. These technologies can transmit the extracted data to business applications like a customer relationship management (CRM) or enterprise resource planning (ERP) system. Whether you’ve approved 100 applications or 1,000, you won’t have to spend a single minute migrating data manually when you use these technologies.

Insights for Continuous Improvement

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

An automated system can offer four types of analytics:

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

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

Better Customer Experience

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

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

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

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

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

Automate Loan Origination with Blanc Labs

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

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

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

Articles

Banking Automation: The Complete Guide

Banks are process-driven organizations. Processes ensure accuracy and consistency across the organization. They are also repetitive. Over the past decade, the transition to digital systems has helped speed up and minimize repetitive tasks. But to prepare yourself for your customers’ growing expectations, increase scalability, and stay competitive, you need a complete banking automation solution.

Systems powered by artificial intelligence (AI) and robotic process automation (RPA) can help automate repetitive tasks, minimize human error, detect fraud, and more, at scale. You can deploy these technologies across various functions, from customer service to marketing.

Many, if not all banks and credit unions, have introduced some form of automation into their operations. According to McKinsey, the potential value of AI and analytics for global banking could reach as high as $1 trillion.

If you are curious about how you can become an AI-first bank, this guide explains how you can use banking automation to transform and prepare your processes for the future.

What is Banking Automation?

Banking automation involves automating tasks that previously required manual effort.

For example, banks have conventionally required staff to check KYC documents manually. However, banking automation helps automatically scan and store KYC documents without manual intervention.

Cost saving is generally one of RPA’s biggest advantages.

According to a Gartner report, 80% of finance leaders have implemented or plan to implement RPA initiatives.

The report highlights how RPA can lower your costs considerably in various ways. For example, RPA costs roughly a third of an offshore employee and a fifth of an onshore employee.

You can make automation solutions even more intelligent by using RPA capabilities with technologies like AI, machine learning (ML), and natural language processing (NLP). According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry.

With that in mind, let’s look closely at RPA and how it works.

Generative AI and Banking Automation

The latest trend in banking automation is the use of Generative AI.

According to Insider Intelligence’s ChatGPT and Generative AI in Banking report, generative AI will have the greatest impact on data-rich sectors such as:

Retail banking and wealth: Generative AI can create more accurate NLP models and help automated systems process KYC documents and open accounts faster.
SMB banking: Generative AI can help interpret non-numeric data, like business plans, more effectively.
Commercial banking: Generative AI will enable customers to get answers about financial performance in complex scenarios.
Investing banking and capital markets: Banks could use generative AI to stress test balance sheets with complex and illiquid assets.

Banks are already using generative AI for financial reporting analysis & insight generation. According to Deloitte, some emerging banking areas where generative AI will play a key role include fraud simulation & detection and tax and compliance audit & scenario testing.

What is RPA?

Robotic process automation, or RPA, is a technology that performs actions generally performed by humans manually or with digital tools.

Say you have a customer onboarding form in your banking software. You must fill it out each time a customer opens an account. You’re manually performing a task using a digital tool.

RPA can perform this task without human effort. The difference? RPA does it more accurately and tirelessly—software robots don’t need eight hours of sleep or coffee breaks.

You can implement RPA quickly, even on legacy systems that lack APIs or virtual desktop infrastructures (VDIs).

Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks.

You can use RPA in banking operations for various purposes.

For example, Credigy, a multinational financial organization, has an extensive due diligence process for consumer loans.

The process was prone to errors and time-consuming. The company decided to implement RPA and automate the entire process, saving their staff and business partners plenty of time to focus on other, more valuable opportunities.

The Need for Automation in Banking Operations

Banks need automation to:

Deliver better customer experiences
Increase online security

Improve decision making
Empowering employees

Below, are more reasons for your bank to automate operations.

To Deliver Faster, Personalized Customer Experiences

New-gen customers want banks that can provide fast financial services online.

The 2021 Digital Banking Consumer Survey from PwC found that 20%-25% of consumers prefer to open a new account digitally but can’t.

Thanks to the pandemic, the shift to digital has picked up pace. A digital portal for banking is almost a non-negotiable requirement for most bank customers.

In fact, 70% of Bank of America clients engage with the bank digitally. The bank’s newsroom reported that a whopping 7 million Bank of America customers used Erica, its chatbot, for the first time during the pandemic.

A chatbot can provide personalized support to your customers. A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions.

A chatbot is a great way for customers to get answers, but it’s also an excellent way to minimize traffic for your support desk.

To Improve Cybersecurity

Cybersecurity is expensive but is also the #1 risk for global banks according to EY. The survey found that cyber controls are the top priority for boosting operation resilience according to 65% of Chief Risk Officers (CROs) who responded to the survey.

Using automation to create a cybersecurity framework and identity protection protocols can help differentiate your bank and potentially increase revenue. You can get more business from high-value individual accounts and accounts of large companies that expect banks to have a top-notch security framework.

Automating cybersecurity helps take remedial actions faster. For example, the automated system can freeze compromised accounts in seconds and help fast-track fraud investigations.

Of course, you don’t need to implement that automation system overnight. With cloud computing, you can start cybersecurity automation with a few priority accounts and scale over time.

For Better Decision Making

AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns.

These insights can improve decision-making across the board. For example, using these insights in your marketing strategy can help hyper-target marketing campaigns and improve returns.

Moreover, these insights help deliver greater value to customers. By making faster and smarter decisions, you’ll be able to respond to customers’ fast-evolving needs with speed and precision.

As a McKinsey article explains, banks that use ML to decide in real-time the best way to engage with customers can increase value in the following ways:

Stronger customer acquisition: Automation and advanced analytics help improve customer experience. They help personalize marketing across the customer acquisition journey, which can improve conversions.
Higher customer lifetime value: You can increase lifetime value by consistently engaging with customers to strengthen relationships across products and services.
Lower operating costs: Banks can reduce costs by fully automating document processing, review, and decision-making.
Lower credit risk: Banks can screen customers by analyzing behavior patterns that signal higher default or fraud risk.

To Empower Employees

As you digitize banking processes, you’ll need to train employees. Reskilling employees allows them to use automation technologies effectively, making their job easier.

Your employees will have more time to focus on more strategic tasks by automating the mundane ones. This results in increased employee satisfaction and retention and allows them to focus on things that contribute to your topline — such as building customer relationships, innovating processes, and brainstorming ways to address customers’ most pressing issues.

Challenges Faced by Banks Today

Here are some key challenges that banks face today and how automation can help address them:

Inefficient Manual Processes

Manual processes are time and resource-intensive.

According to the 2021 AML Banking Survey, relying on manual processes hampers a financial organization’s revenue-generating ability and exposes them to unnecessary risk.

The simplest banking processes (like opening a new account) require multiple staff members to invest time. Moreover, the process generates paperwork you’ll need to store for compliance.

While you complete the account opening process, the customer is on standby, waiting to start using their account.

The slow service doesn’t exactly make a great impression. Customers want to be able to start using their accounts faster. If you’re too slow, they’ll find a bank that offers faster service.

Automation helps shorten the time between account application and access. But that’s just one of the processes that automation can speed up.

Technologies like RPA and AI can help fast-track processes across departments, including accounting, customer support, and marketing.

Automation Without Integration

Banks often implement multiple solutions to automate processes. However, often, these systems don’t integrate with other systems.

For end-to-end automation, each process must relay the output to another system so the following process can use it as input.

For example, you can automate KYC verification. But after verification, you also need to store these records in a database and link them with a new customer account. For this, your internal systems need to be integrated.

Connecting banking systems requires APIs. Think of APIs as translators. They help two software solutions communicate with each other. A system can relay output to another system through an API, enabling end-to-end process automation.

Increase in Competition

Canadians want more competition in banking. The competition in banking will become fiercer over the next few years as the regulations become more accommodating of innovative fintech firms and open banking is introduced.

An increase in competition will give customers more power. They’ll demand better service, 24×7 availability, and faster response times.

You’ll need automation to achieve these objectives and make yourself stand out in the crowd.

Benefits of Automation in Banking

Once you invest in automation, you can expect to derive the following benefits:

Improves Operational Efficiency

An error-free automation system can supercharge operational efficiency.

You’ll have to spend little to no time performing or monitoring the process. Moreover, you’ll notice fewer errors since the risk of human error is minimal when you’re using an automated system.

Implementing automation allows you to operate legacy and new systems more resiliently by automating across your system infrastructure. This increases efficiency, consistency, and speed.

Makes Processes Scalable

Banks noticed how automation could be an excellent investment during the pandemic. As explained in a World Economic Forum (WEF) article:

“Through the combination of a distinct data element with robotics process automation, it is possible to generate client documentation from management tools and archives at a high frequency. Due to its scalability, high volumes can be managed more efficiently.”

The article provides the example of Swiss banks. During the pandemic, Swiss banks like UBS used credit robots to support the credit processing staff in approving requests. The support from robots helped UBS process over 24,000 applications in 24-hour operating mode.

In addition to RPA, banks can also use technologies like optical character recognition (OCR) and intelligent document processing (IDP) to digitize physical mail and distribute it to remote teams.

Cost Reduction

Automation helps reduce costs on multiple fronts:

a. Stationery

80% of banks still favor some form of print statements. The cost of paper used for these statements can translate to a significant amount. Automation and digitization can eliminate the need to spend paper and store physical documents.

b. Human error

Human error can require reworks and cause delays in processing customer requests. Errors can result in direct losses (like a lost sale) and indirect losses (like a lost reputation). Minimizing errors can help reduce the cost associated with human error.

c. Increased employee satisfaction

You’ll spend less per unit with more productive employees. Automation can help improve employee satisfaction levels by allowing them to focus on their core duties.

For example, a sales rep might want to grow by exploring new sales techniques and planning campaigns. They can focus on these tasks once you automate processes like preparing quotes and sales reports.

Working on non-value-adding tasks like preparing a quote can make employees feel disengaged. When you automate these tasks, employees find work more fulfilling and are generally happier since they can focus on what they do best.

Happiness makes people around 12% more productive, according to a recent study by the University of Warwick.

As Professor Sgroi explains, “The driving force seems to be that happier workers use the time they have more effectively, increasing the pace at which they can work without sacrificing quality.”

Customer Satisfaction

Automation can help meet customer expectations in various ways.

Speed is one of the most difficult expectations to meet for banks. You want to offer faster service but must also complete due diligence processes to stay compliant. That’s where automation helps.

61% of customers feel a quick resolution is vital to customer service. As a bank, you need to be able to answer your customers’ questions fast.

How fast? Ideally, in real-time.

A level 3 AI chatbot can help provide real-time, personalized responses to your customers’ questions.

In addition to real-time support, modern customers also demand fast service. For example, customers should be able to open a bank account fast once they submit the documents. You can achieve this by automating document processing and KYC verification.

Better Risk Management

Automation can help minimize operational, compliance, and fraud risk.

Since little to no manual effort is involved in an automated system, your operations will almost always run error-free.

You can also automate compliance processes. For example, you can add validation checkpoints to ensure the system catches any data irregularities before you submit the data to a regulatory authority.

Automation can help minimize fraud risk too. Using AI and ML can help flag suspicious activities and trigger alerts. As this study by Deloitte explains:

“Machine learning can also analyze big data more efficiently, build statistical models quickly, and react to new suspicious behaviors faster.”

Using traditional methods (like RPA) for fraud detection requires creating manual rules. RPA works well in a structured data environment. But given the high volume of complex data in banking, you’ll need ML systems for fraud detection.

Blanc Labs’ Banking Automation Solutions

Blanc Labs helps banks, credit unions, and Fintechs automate their processes. We tailor-make automation tools and systems based on your needs. Our systems take work off your plate and supercharge process efficiency.

Our team deploys technologies like RPA, AI, and ML to automate your processes. We integrate these systems (and your existing systems) to allow frictionless data exchange.

Book a discovery call to learn more about how automation can drive efficiency and gains at your bank.

Articles

The Complete Guide to Intelligent Document Processing

Intelligent document processing (IDP) helps companies manage documents more efficiently and digitizes unstructured data from multiple sources.

IDP is part of modern digital transformation, which is changing how businesses operate. Artificial intelligence (AI) is one of the key drivers of digital transformation. AI makes business processes more efficient, reduces costs, and improves customer experiences. According to an IBM study conducted in 2022, AI helped:

54% of businesses reduce costs with efficiency
53% of businesses improve their IT or network performance

48% of businesses improve customer experience

IDP offers similar benefits. It encompasses multiple technologies, including AI, machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and optical character recognition (OCR) to automate your document processing workflow.

That’s barely scratching the surface of what IDP has to offer a modern business. This guide explains the meaning of IDP and how modern businesses can use it to their advantage.

What is Intelligent Document Processing?

Intelligent document processing is a technology that allows businesses to digitize unstructured data from multiple sources of documents. For example, your business may need to manage unstructured data from online survey forms, word files, PDFs, and similar document types.

Imagine manually scanning through each of these documents to extract information, convert them into digital documents, and organize the data. You’ll end up wasting resources and time on a mundane task.

Fortunately, IDP can automate the entire process. Automating the documentation workflow enables you to free up your team’s time for more value-adding tasks. Moreover, you also spend less on handling and routing these documents and errors your team might commit during the process.

Here are five technologies IDP uses to automate your documentation workflow:

Robotic Process Automation (RPA)

RPA is integral to IDP — it’s often even confused with IDP, but they’re technically different. RPA is a technology used in building software robots that automate tasks that otherwise require human effort.

For example, RPA can help populate data from a document into your ERP without involving any humans. This translates to greater efficiency because RPA is faster and doesn’t need coffee breaks.

However, IDP goes a step further. IDP combines the power of RPA and AI. RPA is a rules-based technology that can’t make data-driven decisions. On the other hand, AI can perform more complex tasks.

Artificial Intelligence (AI)

IDP uses AI technologies like machine learning and natural language processing for data extraction, document classification, and claims processing. For example, AI reads and labels information on documents and can accurately route documents without requiring manual effort.

If you’re in banking, AI can help automatically classify mortgage documents, tax records, and pay stubs. IDP can also automate claims processing using AI. For example, it can find the relevant customer for a specific claim and then route it to the appropriate department.

Machine Learning (ML)

ML is a branch of AI that allows an algorithm to learn as it processes more data. Over time, ML helps IDP extract data from documents more accurately.

For example, the ML algorithm uses pre-processed documentation to collect information, including names, amounts, and dates. It stores the data and further analyzes it. This way, it can process future documents more accurately.

Natural Language Processing (NLP)

NLP is a branch of AI that allows computers to understand text and speech, just like humans. IDP relies on NLP to understand data faster. It does so using sentiment analysis and tags language elements like named entities to derive context.

As you can imagine, NLP plays a critical role in understanding the contents of a document and data extraction.

Optical Character Recognition (OCR)

OCR is one of the key technologies used for processing handwritten or scanned documents. With OCR, IDP can copy text from a document image — text that a computer can’t directly copy into its system. OCR converts the information in the document image into editable text, which allows for processing and storing this information.

IDP Vs. OCR

Traditional OCR has limited capabilities. For example, it can only read templatized documents formatted using specific rules. OCR is also limited to just extracting text and can’t derive any context by itself, which means it can’t make any decisions for you.

As a result, OCR fails to process unstructured or handwritten documents, rendering it less valuable for modern businesses and limiting scalability.

On the other hand, IDP combines OCR with other AI-based technologies and RPA for extraction, context, and execution.

A traditional bank check is a classic use case of IDP. Suppose you’re a multinational financial organization that processes hundreds of checks daily. You receive checks from different banks that use a different format. Each issuer has different handwriting. No two checks look the same, so OCR can’t process these checks accurately.

However, IDP can process these checks far more accurately. IDP uses OCR to convert handwritten and scanned data into text. Then, it uses NLP to derive context about the text extracted from the scanned check. The RPA takes over and executes an action based on a preconfigured set of rules. Over time, the ML algorithm gets better at processing checks.

Read more: IDP vs RPA vs OCR

How Does IDP Work?

IDP uses a five-step process for document processing:

Document pre-processing
Data Capture
Document Classification
Document Extraction
Document Verification
Integration

Document Pre-processing

Before we begin processing documents, they must be cleaned up for ‘noise’ so that they become machine readable. The quality of pre-processing often determines the accuracy of the final result. Reducing noise may include splitting sentences into words, lower-casing words (e.g., the word Bank and bank mean the same but are represented as two separate words in certain document processing models), removing stop words like ‘a’, ‘an’, ‘the’, etc. It may also involve improving image quality for better readability.

Data Capture

Data capture (or ingestion) is the first step, where you input the document into the process. OCR and the ML algorithm are key technologies used for data capture in IDP.

OCR is available on many commonly used tools like Microsoft Office. This means you don’t necessarily need an IDP system to use OCR, but you do need OCR to use IDP. OCR captures data from the document, whether it’s an image or digital document, and sends it to the IDP system for extraction.

Document Classification

We briefly discussed NLP in the previous section on data extraction. However, NLP has an even bigger role to play when classifying documents.

IDP systems use NLP, OCR, and long-term short memory (LTSM) to analyze and classify data. NLP and transformer models (first described in a 2017 paper from Google) establish a relationship between words in a sentence and assign weightage to each word to interpret the meaning.

Practically, IDP systems classify documents and extract data simultaneously. The system typically takes less than a few seconds to classify documents and extract data.

Document Extraction

Document data extraction involves converting the captured data into usable data. IDP uses NLP and ML models to understand data and derive context.

Structured: Data stored in an Excel sheet is a great example of structured data. It’s a data set where the system doesn’t need additional context to interpret the data.
Semi-structured: Semi-structured data is where part of the data is structured. Examples include invoices, annual reports, and contracts.
Unstructured: Unorganized data doesn’t follow any specific format and is often received in multiple types, including images. This type of data is the most difficult to process automatically. However, 80% to 90% of data organizations collect is unstructured, making it mission-critical for you to have the tools that allow processing unstructured data.

Structured data is easy to interpret, while interpreting unstructured data requires additional technologies like NLP.

The extraction process involves two aspects:

Textual data extraction: Textual data extraction involves identifying text in a particular document. The IDP system uses ML to identify and tabulate the text based on specific semantic parameters. The best IDP systems use entity extraction techniques like convolutional neural networks (CNNs), that allow the IDP system to extract data from documents that don’t follow any specific format.

Visual data extraction: This is more complex because it involves understanding elements like signatures and logos. The IDP system must detect, understand, and extract information from visual elements while using ML to understand the element’s structural relationship and relevance.

The best IDP systems offer accurate textual and visual data extraction. You can use them to extract data from multiple document types with great accuracy.

Document Verification

IDP verifies and validates document data for accuracy. It ensures that it’s extracting the right data from the document and that the extracted data is accurate.

KYC verification is an example of document verification. When customers provide an ID and complete the KYC form, you’ll need to verify these details against a database. However, you can eliminate manual effort and validate KYC data automatically using IDP.

Automated validation is especially helpful when you’re processing documents at scale. For example, a receipt might be mixed up with one of your invoice batches. The IDP system needs to be able to differentiate and disregard this document through validation.

Validating borrowers by approved vendors is an excellent example of data validation. You can use the IDP system to identify borrowers who have availed loans from an approved lender. You can automatically mark such borrowers during the extraction process without any manual effort.

Integration

Once the IDP system completes processing the data, it will create a JSON or XML output file containing the compiled data.

You can also use APIs to migrate this information to a data repository or third-party tools like enterprise resource planning (ERP) or customer relationship management (CRM) systems. If your IDP system doesn’t integrate with your business solutions, we can help you integrate any API-enabled application with your IDP.

Benefits of Intelligent Document Processing

Using IDP offers monetary as well as non-monetary benefits.

Minimizes Human Error

Manually scanning documents and migrating data is prone to human error, especially when processing a high volume of documents. Errors can be expensive—you might upset your customers, disrupt your workflow, or become non-compliant.

IDP helps nearly eliminate the risk of human error from your processes. As long as the data on the physical documents is accurate, the system will make sure everything that goes into your systems via the IDP is accurate.

Better Employee Experience

Automating document processing saves time and effort so your team can focus on more productive tasks.

Our partner, UiPath, surveyed 4,500 office workers worldwide and found 43% of employees believe automation allows them greater opportunities to focus on more important work.

The same UiPath survey also reveals that 52% of employees believe automation helped them achieve a better work-life balance.

Lower Compliance Risk

IDP helps streamline compliance processes. An IDP system automatically extracts relevant information from documents and classifies them based on predefined criteria, which means fewer errors and easily accessible records you might need for compliance.

You can configure the IDP system to compile data on a searchable database, which helps simplify audits by making information readily available. The best IDP systems can also detect sensitive information and determine how to treat it based on sensitivity.

Improves Customer Experience

Fewer errors, faster turnarounds, and frictionless onboarding can greatly enhance customer experience.

Automated document processing allows you to serve your customer better in almost all client-facing functions. For example, if a customer submitted KYC forms last week and calls support to ask if KYC verification is complete, you’ll need to sift through a pile of paperwork to provide an answer.

On the other hand, if you use an IDP system, you can search the database and answer them faster. Customers don’t like being on hold—and when you use an IDP system, they won’t have to.

Scale Document Processing

As your business grows, you’ll need to process more documents. Manually processing documents can be resource-intensive.

Your team will spend a ton of time scanning documents and extracting and transferring data to your internal systems. You’ll need to keep adding more people to the team, which means you’ll essentially be investing money in mundane tasks.

Automating mundane tasks allows your team to focus on parts of the business that require a human touch. For example, a sales rep can work on selling—the task you hired them for—instead of collecting KYC forms.

Improves Data Usability

A large portion of your business’s data is unstructured. Similarly, a good volume of business data is locked behind PDF files, emails, and scanned copies of documents. IDP systems help structure this data, making it usable.

This means data previously lying dormant can help you make more insightful decisions once you start using an IDP tool. Digital documents are a critical source of information, provided you handle them correctly. As a McKinsey article explains:

“Incoming mail and other physical documents are an important source of data, but not the only one—many documents that arrive digitally can pose significant challenges if not handled correctly. Emails, for example, may require significant effort to become structured, digital data that can be processed automatically.”

Intelligent Document Processing with Blanc Labs

If you need to implement a comprehensive, intelligent document processing system, we can help. Blanc Labs works with financial organizations like banks and credit unions to automate their workflow. We can help you build a customized automation system based on your specific needs and internal workflows to make your document processing seamless.