Corporate lending processes in many banks and other financial institutions are subject to back-office activities, which typically have manual interventions.
Before the pandemic, many banks — including incumbents — lagged because of multiple legacy systems operating in silos at their back offices. Little action (if any) is taken to address these issues because the return on investment (ROI) in the short-term is unjustifiable.
Post-pandemic, lenders may have to grapple with several pressing challenges, including:
Rising defaults in credit card or mortgage dues
Business loans with increasing non-performing assets
Shrinking margins and revenue due to reduced interest rates
Pressure to waive some existing fees/charges and offer loan moratoriums to support customers, which might create a liquidity crunch for lenders
Need to reinvent operating models for seamless product and service delivery
Potential for fraud and cyber threats as bad actors exploit the “new normal”
Maintaining the same level of staff efficiency that existed before the pandemic
Surge in traffic at contact centers adding more pressure to support agents
Lenders can no longer depend on manual interventions, which impact have an impact on an organization’s operational efficiency, ability to handle risks, and revenues.
Instead, they must rethink their operational strategies, and implement RPA to automate their manual processes, to overcome these challenges.
Why use RPA in lending?
For any financial institution, lending is a critical service area. Its time-consuming, rule-based, and process-driven nature makes it suitable for automation.
RPA automates various tasks crucial to the lending process, from initiating loans to processing documents to quality control, and more. This means faster loan application approvals and ultimately increased customer satisfaction.
In addition, RPA handles the mundane and rote tasks, freeing up employees to focus on more strategic work and improving productivity levels.
For example, Sumitomo Mitsui—a Japanese banking company—slashed 400,000 hours of manual labor (an entire year’s work for 200 people) for their workers by automating their processes.
The firm introduced RPA systems to cut out unnecessary work and collect client data. This provided a better retail banking experience for each of their customers whenever they had appointments at the bank.
While entrenched obstacles like monolithic systems, siloed business priorities, and manual processes frustrate lenders, RPA saves the day by:
Saving time and money: Software robots process higher volumes of loan applications than human employees, cutting down timelines and saving on labor costs and IT spending.
Increasing efficiency: Automation improves business processes leading to minimal losses and maximum loan repayments as loans are disbursed only to eligible applicants. Plus, employees achieve timelines as quickly as possible, thereby increasing efficiency.
Improving productivity: RPA bots execute rote, repetitive tasks, freeing up employees to focus on more high-value projects.
Handling support queries quickly: Virtual assistants or chatbots can answer customers’ queries, instantly supporting them with enough information to continue with the loan application process.
Eliminating fraud: RPA allows lenders to integrate automated regulation checks for faster, more intuitive, and efficient workflows. And with the addition of AI and machine learning (ML), RPA bots’ capabilities can go further to gather, store, analyze, and execute purpose-designed anti-fraud measures in the lending processes.
Ensuring business continuity: Financial service providers that automate some or most of their lending processes can scale better and manage the impact of any future disruptions.
OCBC, a Singaporean bank, also implemented RPA in their home loan application processes. The bank realized lower operation costs and decimated the time taken to adjust and process home loans from 45 minutes to one minute.
OCBC bank’s financial services are more accessible to its customers. RPA bots handle the home loan repricing eligibility verification processes and draft recommendation emails with the best re-priced options.
Instead of laying off its employees, the bank also redeploys them for other operations.
Business use cases for RPA in lending
Financial institutions that leverage RPA have seen first-hand how this automation technology is both cost-effective and labor-saving.
As RPA gains popularity in the finance vertical, lenders are building on their existing tech stacks to solve increasingly complex challenges.
If applied to the right use cases, RPA can help lenders move up the value chain.
Here are some prominent business use cases that help lenders achieve operational and business process efficiency with RPA.
Loan application processing
Post-pandemic, lenders are under greater pressure to rectify their lending processes and ensure maximum loan repayments with minimal losses.
That’s why the loan application process typically involves complex approvals, such as collecting client data, as well as checking borrower credit ratings and previous borrowings, before approving or rejecting the request.
It can take around 15 minutes to process each detail. RPA bots take up the loan processing task, reducing the time taken to less than five minutes while helping the loan processing officer achieve quick timelines and efficiency.
RPA bots can execute tasks on the employees’ behalf, even in their absence, and expedite the loan processing function faster and more accurately. Bots also provide the processing officers with details of the loan application for further analysis on an as-needed basis.
For instance, if a customer applies for a home loan, lenders approve such applications based on the applicant’s income, credit ratings, or existing loans. The loan processing officer will share these details with the sanctioning authority and avail them on credit rating sites, among other sources.
RPA bots can quickly obtain such details while:
Saving employees’ time and effort
Reducing turnaround time
Increasing employee efficiency and productivity
Improving customer satisfaction
Giving you an edge over the competition
Bringing digital transformation in your business
In the mortgage industry, lenders begin by collecting and verifying borrower information from W-2 and 1003 mortgage loan application forms.
RPA bots scan the forms to confirm that all required fields are completed and identify any discrepancies in the data. This reduces risks, such as denial of mortgage loans by underwriters, foreclosures, and lawsuits.
Plus, bots help mortgage lenders by flagging missing details in the mortgage process or performing credit checks on the Loan Origination System (LOS) to find blacklisted loan portfolios.
Underwriting
Underwriting is a critical part of risk management, which involves vetting applicants who pose unacceptable risks while inspecting their creditworthiness to determine whether to approve them for a loan.
The lender has to determine if you can pay back the loan before approving or rejecting your application.
In mortgage underwriting, for example, a mortgage broker or loan officer collects the application documents, verifies your identification, credit history, and financial situation, among other risk factors.
As an often manually handled, error-prone process, underwriting can delay turnaround times and incur high costs, resulting in inefficient processes and financial loss.
RPA fixes these gaps, ensuring faster processing, fewer errors, cost efficiency, higher productivity, shorter client wait times, and happier customers.
“The volume of daily customer inquiries we receive (ranging from balance inquiries to general account information) is high, making it difficult for bank employees to respond quickly. The RPA implementation solution has assisted us in automating routine, rule-based operations, allowing us to respond to queries in real-time and reduce turnaround time.” — Tracy Acker, CEO of GetPaydayLoan.
Fraud detection
The financial services industry saw a 60.5% global increase in digital fraud attempts from 2019 to 2022, meaning lenders must put more effort into mitigating risks and losses.
To determine the risk of a customer’s loan request, financial institutions today submit requests through the LOS, which are reviewed based on specific rules. A team of analysts access external sources to check and potentially flag loans, alert risks, recommend next steps, and share the results on the LOS.
The routine nature of these well-defined, rule-based steps makes them suitable for automation.
RPA bots can access external websites to identify and flag potential risks, record borrower details, and transfer data to the system. This reduces loan processing time and errors while also improving the workflow.
With AI-powered innovations like Inscribe, it’s easier to detect fraud, reduce risk exposure, and authenticate customers.
Inscribe’s enhanced automation features, including Optical Character Recognition (OCR) and an AI-powered image fraud detection, reduce risk while saving time on document reviews. Plus, you can understand the potential risk of opening credit lines while reducing reliance on asset-backed financing and credit scores.
Loan closing
Loan closing is another critical step that requires detailed cross-checking, reporting, and auditing across systems and applications. It’s tedious, time-consuming, and mundane even for experienced and efficient loan officers, underwriters, and processors.
RPA bots eliminate the need for human intervention when cross-checking documents and fixing any errors. Bots fulfill loan closing duties, including calculating escrow amounts and auto-generating closing disclosure.
The result? Faster processing turnaround times and a better customer experience.
Loan disbursement
When the U.S. Small Business Administration (SBA) initiated the $349 billion PPP loan program, registered lenders had to process all loan applications, get payroll verification documents, and disburse the loans.
The program provided 11.47 million loans totaling $792.6 billion, leading to significant processing backlogs as businesses rushed to submit their applications for approval.
Many banks manually processed and submitted the loan applications to the SBA for funding—a tedious, repetitive, and time-consuming process.
RPA dramatically streamlines such manual processes, expediting the application and submission process. Bots quickly extract data from borrower application forms to populate fields in the SBA E-Tran website, update the lender database and loan calculators, and complete the application forms for loan guarantee.
Automating the loan disbursement process helps lenders:
Save time and costs
Reduce human errors
Eliminate manual data entry
Automatically manage and move data between systems or applications
Manage the influx of loan applications they receive
Accelerate distribution of critical funds to their customers
Improve lending workflows for long-term success
Traditional lending processes are cumbersome, slow, and often depend on manual work. This is not only tedious for the lender, but it also causes unnecessary frustration for borrowers, impacting the customer experience and your ability to scale.
Inscribe eases the frustrations elicited by the slow, disjointed corporate lending processes and the hurdles lenders face when trying to resolve those issues.
We help you create a transparent, efficient, and automated workflow for your clients so you can make smarter lending decisions quicker, and finalize loans ahead of deadlines.
if you believe your business will benefit from an automation solution, check out our automation solutions which includes a free RPA consultation.
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