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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.

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10 Tips to Successfully Implement RPA in Finance

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

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

What is Robotic Process Automation (RPA)?

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

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

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

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

The Use of RPA in Banking Automation

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

Customer Service Automation

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

Loan Processing

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

Fraud Detection and Prevention

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

KYC and AML Compliance

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

Back Office Operations

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

Payment Processing

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

Risk Management

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

Internal Compliance Monitoring

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

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

10 Tips for Successfully Implementing RPA in Finance

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

Start Small

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

Define Clear Goals

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

Involve Stakeholders

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

Assess Processes

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

Choose the Right Tools

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

Define Roles and Responsibilities

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

Ensure Data Security

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

Plan for Scalability

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

Monitor and Evaluate Performance

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

Foster a Culture of Innovation

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

Implementation Automation with Blanc Labs

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

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

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

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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.

Articles

IDP vs. RPA vs. OCR

Financial institutions deal with a lot of data from various sources like forms, emails, invoices, PDFs, etc. daily. Processing this unstructured data manually is time-consuming and requires a lot of effort. It can also be prone to errors, which can have costly consequences. This can take away from the time and energy that employees could be spending on more strategic tasks.

Automation technologies like IDP (Intelligent Document Processing), RPA (Robotic Process Automation), and OCR (Optical Character Recognition) can take manual document processing off your hands.

If you are wondering which one you should choose among IDP vs. RPA vs. OCR for the digital transformation of your financial institution, you are in the right place. Here we break down each banking automation technology to help you choose the best one.

What is RPA?

RPA is a technology that allows organizations to automate repetitive, routine tasks typically performed by humans. These tasks include data entry, document processing, customer service interactions, and back-office functions such as compliance, risk management, and accounting.

What is possible with RPA, and where does it fall short?

Banks are known to be heavily regulated, and compliance is a critical part of banking operations. This is where RPA can play a significant role by automating compliance-related tasks, such as KYC (Know Your Customer), AML (Anti Money Laundering), and other regulatory data management. It can also automate data migration, trade execution, data validation, data updates, and perform simple copy/paste functions.

However, RPA in banking automation also has some limitations. RPA requires a developer or GUI window to operate. Thus, RPA can only be used to automate simple screen-related tasks. It is limited to automating tasks that are highly structured and rule-based and is not suitable for tasks that require human judgment or decision-making.

Also, the entire automation process can break if there is an update in the user interface of a linked software. It is an outdated technology that relies on OCR and is not built for modern end-to-end integration.

What is Intelligent Document Processing (IDP)?

IDP is a next-generation technology designed to tackle the limitations of RPA. It is a system created to process documents just like humans. If you compare IDP with RPA for banking automation, you will find that IDP is the ideal combination of OCR, Artificial Intelligence (AI), machine learning, and natural language processing.

IDP is independent of strict rule-based approaches. Due to its flexibility, it can reach and process unstructured data not reachable via RPA. IDP tools also reduce the margin for errors by validating the data and informing the team in cases that need human intervention.

Here are some reasons why banks need Intelligent Document Processing.

How can IDP help in banking automation?

IDP can be a valuable tool in banking automation in several ways:

Account Opening

You can use IDP to automate extracting information from account opening forms and other related documents. It can reduce the effort and time required for manual data entry and improve the accuracy of the data.

Document Management

IDP can index, classify and route documents to the appropriate systems or individuals. Thanks to intelligent document processing, financial institutions can manage documents effectively and quickly retrieve the necessary information.

Compliance

IDP can help automate extracting information from compliance-related documents for processes such as KYC, AML, and other regulatory requirements. This way, banks can comply with regulations more quickly and efficiently while reducing the risk of non-compliance.

Loan Processing

IDP can enable the extraction of information from loan applications and other related documents such as income statements, credit reports, and real estate appraisals. It can help automate the loan review process, making it faster and more accurate.

Fraud Detection

IDP can be used to extract information from documents and match it with other sources to detect potential fraud. In this way, banks can reduce the risk of fraud and losses.

IDP vs RPA

The choice of automation for document processing boils down to IDP vs. RPA. RPA and IDP are two different technologies used in automation but are sometimes confused with the other.

The main difference between RPA and IDP is that RPA does not have the native intelligence of AI. RPA cannot consume and analyze data on its own. RPA is limited to mimicking repetitive actions performed on computer screens with a mouse and a keyboard. It is helpful for tasks that don’t require high-level decision-making and is largely outdated. It is often said that AI is ‘the brain’ while and RPA is ‘the hands.’

On the other hand, IDP takes automation up a notch by automating documents and absorbing and understanding data to extract actionable insights. Thus, IDP can be considered the future of banking automation.

RPA works better with structured documents (e.g. claim forms, tax forms ) where IDP works better with unstructured documents (e.g. contracts, handwritten notes). Ideally, an organization should use a combination of RPA and IDP to achieve better operational efficiency.

What is OCR?

OCR is a technology used in both RPA and IDP, that reads, extracts, and converts data from images and scanned documents into text for electronic automation and importation. When integrated with automation solutions like IDP or RPA, OCR can efficiently process structured data, eliminating the need for manual data entry and thus minimizing errors. OCR technology also enhances image quality to produce more accurate results.

While OCR is a step towards automation, it is not very effective in processing unstructured data that most banking institutions deal with every day in large volumes.

IDP vs RPA vs OCR

Blanc Labs’ Document Processing Solutions for Banks

Blanc Labs’ helps financial institutions like banks, and credit unions fast-track their way to digital transformation. We can help you integrate powerful automation technologies into your processes to increase productivity and reduce manual labor and the scope for errors.

Our team provides a customized combination of machine learning and artificial intelligence for automating complicated tasks like document processing so that you can save your resources and provide faster and better financial services.

If your financial institution deals with a ton of documents every day, let us help you put your processing on auto. Book a discovery call with us today, and we will create a seamless document processing solution unique to your needs.

News

Blanc Labs and Daylight are teaming up to help enterprise teams simplify rules-based complex processes

Posted on 20 January 202323 January 2023

Blanc Labs, a technology services company specializing in digital transformation for financial services and healthcare is partnering with Daylight to deliver digital solutions focused on simplifying logic and/or rules-based processes.

Complex processes are elaborate labyrinths of different checks and balances. 88% of senior IT leaders in North American agree that optimizing complex processes is both very costly and requires significant resources*. Blanc Labs and Daylight strategic partnership will focus on:

Empowering businesses to easily build digital user experiences that replicate the rules of their business processes
Digitize and automate paper-based processes
Quickly build connections to legacy applications to deliver a modern end-user experience without the need to rip and replace
Optimize ROI from investment in automation by putting business technologists in the driver’s seat

“We are committed to helping organizations transition into the digital age by leveraging our deep sector expertise, ecosystem partners, and world-class tech talent to reduce risk and achieve strategic objectives,” said Hamid Akbari, CEO of Blanc Labs. “We believe our partnership with Daylight will be a game-changer for companies looking to do more with less”.

The partnership will focus on developing and delivering end-to-end solutions without displacing the existing tech stack or disrupting IT.

“Both teams are passionate about empowering teams with the ability to build their own solutions and take charge of improving processes and scaling digital transformation initiatives… I’m excited to see the impact we can make together,” said Art Harrison, CGO of Daylight.

*2022 Daylight Market Survey: 100 IT leaders in North America (Enterprise Orgs with 5000-10000 employees)

About Blanc Labs

Blanc Labs offers technology solutions that help enterprises become ready for the future. Blanc Labs has developed expertise and bespoke solutions in a wide variety of applications, including financial services, healthcare, enterprise productivity, and customer experience, to help companies rapidly deliver on their digital initiatives. Headquartered in Toronto, Blanc Labs serves the Americas through operations in Toronto, New York, Bogota, and Buenos Aires.

For more information, visit www.blanclabs.com

About Daylight

Daylight makes it easy for your business teams to simplify and digitize your current processes through rapid iterations while addressing IT concerns like security, privacy, scalability, and integration. From modernizing legacy systems to improving the flow of data and information between people, processes, and systems, we empower your business users, and IT teams to streamline inefficient processes and free up valuable resources to complete more projects.

For more information, visit daylight.io

Articles

Top Use Cases for Banking Automation

Did you know that the World Bank uses Banking Automation including robotic process automation (RPA) for many of its functions? That’s how powerful it is.

Modern businesses rely on automation to reduce costs and improve efficiency, but how can banks use automation? In this article, we explain the most common use cases of banking automation.

What is Banking Automation?

Banking automation involves handing over repetitive business processes in financial institutions like banks and credit unions to technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML).

Why Banks Need Intelligent Automation

As a banking professional, you know that a good chunk of your daily tasks is repetitive and mundane. Banking automation eliminates the need for manual work, freeing up your time for tasks that require critical thinking.

Here are seven reasons why banks needs intelligent automation:

Better Customer Experience
Automated KYC
Faster Mortgage Application Processing
Accurate Report Generation
Anti-Money Laundering Prevention
Audits and Compliance
Faster Decision Making

Below we dive deeper into banking automation use cases so you can get a closer look at how automation can transform your workflow.

1. Customer Experience

A robust Customer Experience has never been more important. As the world moves online, you’ll need to re-engineer your Customer Experience to make it friction free, faster and more efficient.

Consider a customer’s first experience with the customer onboarding process. If this isn’t a painless experience, you risk turning away a customer in the first interaction.

Sure, you might need to invest some money to improve the customer experience and make it seamless and efficient, but the potential ROI is excellent. Think about it. Automation will eliminate much of the manual and low-value in-person interaction, saving your sales reps plenty of time to focus on running effective sales campaigns.

Automating processes reduce the potential for errors, allowing you to onboard customers faster. Automation also reduces costs because it eliminates the manual labor and paperwork associated with customer onboarding.

Moreover, your customers will be able to use their accounts faster, which improves customer experience. As a McKinsey report explains:

“Automation and artificial intelligence, already an important part of consumer banking, will penetrate operations far more deeply in the coming years, delivering benefits not only for a bank’s cost structure, but for its customers.”

2. KYC

Most banks perform KYC (Know Your Customer) by manually verifying customer details. The problem? Manual verification can take plenty of hours.

The KYC process doesn’t end at verification. You must manage KYC documents for a long time to comply with regulatory requirements. Using automation in banking operations can help free up the hours you spend on manual verification.

Moreover, automation also eliminates the risk of human error. By eliminating room for error, automation ensures improved customer experience, increased quality assurance, and the number of cases processed each month, according to a McKinsey study.

3. Mortgage Application Processing

Mortgage application processing involves plenty of paperwork. Manually checking details on each document is time-consuming and leaves room for error. On the other hand, intelligent document processing (IDP) helps streamline document management.

Remember those desks full of paperwork? That’s a thing of the past. Modern banks use IDP to manage documents digitally. IDP helps automate the generation of customer risk profiles and mortgage document processing, reducing processing time to a few days.

An Accenture study found that 47% of customers prefer opening a new bank account online using a computer, while 37% prefer using the bank’s app or website.

The shifting consumer preferences point to a future where loan requests and processing are online and automated. Now is a great time to prepare your bank for that future.

4. Report Generation

All financial institutions need to generate reports for various purposes. For example, you might need to generate a report to show quarterly performance or transaction reports for a major client.

RPA can help generate reports automatically. The system can auto-fill details into a report and prepare an error-free report within seconds. An automated system can perform various other operations as well, such as extracting data from internal or external systems and fact-checking the reports.

5. Anti-Money Laundering (AML) Prevention

In Canada, banks need to ensure they are complying with the statutes of the Proceeds of Crime (Money Laundering) and Terrorist Financing Act, 2000. Depending on your location, compliance requirements might include ongoing risk-based assessment, customer due diligence, and educating staff and customers about AML laws.

A single AML investigation can take 30 minutes or more when assigned to an employee. However, automation can complete the same investigation much faster and minimize errors.

Using automation ensures you don’t spend too much money on AML investigations and stay compliant, so you don’t have to pay hefty fines.

6. Audits and Compliance

The cost of maintaining compliance can total up to $10,000 on average for large firms according to the Competitive Enterprise Institute.

Maintaining compliance is expensive but less so than being non-compliant. For example, banks must ensure data accuracy when producing loan facility letters. However, instead of requiring employees to spend time meticulously verifying customer data, you can use intelligent document processing to save time and guarantee data accuracy.

Banking automation can help you save a good amount of money you currently spend on maintaining compliance. With automation, you can create workflows that satisfy compliance requirements without much manual intervention. These workflows are designed to automatically create audit trails so you can track the effectiveness of automated workflows and have compliance data to show when needed.

For example, you can set up a system to auto-freeze compromised accounts. Once the account is frozen, RPA can automatically complete the steps in your fraud investigation process. The system also creates an audit trail in the process.

Unlike humans, RPA can be active 24/7. Using RPA in banking can help ensure the accuracy of compliance processes, ensuring you’re compliant at all times without investing a lot of human resources towards compliance.

7. Decision Making

Managers at financial institutions need to make decisions about marketing, operations, and sales, but relying on raw data or external research doesn’t provide full context. RPA can help compile and analyze internal data to track client spending patterns and preferences.

AI and RPA-powered automation can help make decisions about timing marketing campaigns, redesigning workflows, and tailor-making products for your target audience. As a result, you improve the campaign’s effectiveness, process efficiency, and customer experience.

For example, when introducing a mortgage loan product, you can use RPA’s data analysis capabilities to identify location-specific and borrower-specific risks. RPA can compile a summary of the risks on a document, allowing the credit manager to study risk profiles without the associated manual work.

Blanc Labs’ Banking Automation Solutions

Blanc Labs works with financial organizations like banks, credit unions, and Fintechs to automate their processes.

We can create tailor-made automation software solutions based on your banks’ needs to minimize manual work and improve process efficiency. Our team can help you automate one or multiple parts of your workflow using technologies like RPA, AI, and ML.

Book a discovery call with us to see first-hand how automation can transform your bank’s core operations. We’ll create an automation solution specifically for your organization that works in tandem with your current internal systems.

Articles

The Transformative Power of Banking Automation

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

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

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

What is Banking Automation?

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

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

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

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

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

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

Why Banks Need Banking Automation

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

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

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

How Banking Automation Can Transform Your Bank

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

Banking Automation Leads to Efficiency

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

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

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

Better Customer Experience

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

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

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

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

Compliance and Risk Reporting

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

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

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

Reduced Costs

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

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

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

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

Automation and Adaptability

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

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

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

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

The Bottom Line

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

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