Move the loan approval process 30% faster with AI

Artificial intelligence is disrupting industries across the globe, and lending is far from immune. Research by Accenture suggests that lending is an industry that will see some of the most dramatic AI transformation. The same research suggests that at US banks, just 27% of employee time has “low potential” for AI transformation – leaving 73% of the workday open for improvement.

Whether this transformation comes via augmented AI practices or through automated workflows, there is a whole new world of possibilities. However, even as millions of consumers pick up tools like ChatGPT and Microsoft Copilot, heavily regulated industries like banking will be slower to adapt to AI. Because of this delay, those who prioritize early AI adoption may end up overtaking competition – becoming industry leaders over time.

As for lenders, a survey of finance executives conducted by KPMG suggests that the time has come to scale. With 39% of CEOs suggesting that their generative AI tools have moved out of the pilot phase and into implementation, there has never been a better time for lenders of all kinds to pick up AI. Here’s why:

AI speeds up loan underwriting and borrower assessment

Underwriting is a critical part of offering a loan, and the foundation for the lender’s profit or loss from the deal. Whether you’re a credit manager at a bank or working in the finance department at a car dealership, you’ll likely end up processing vast amounts of financial data, often by hand. Unlike manual document review (such as inputting the borrower’s income, calculating the utilization ratio, or reviewing supporting documents such as pay stubs), AI tools can help spot errors, flag inconsistencies, and run custom metrics to generate a borrower score. All of this results in a smoother, speedier loan. 

Traditional loan processing requires a significant amount of effort, paperwork, and communication – moving papers between the buyers, the dealers, the lenders, and back again. With AI tools to power workflows, handoffs, and document verification, lenders may end up drastically reducing both timeframes and costs.

AI tools also allow for fewer human errors in document handling, especially when it comes to banking information or direct deposits. Loans repaid weekly, monthly, or bi-weekly result in hundreds, if not thousands, of banking transactions every day. With AI-powered software tools, this data can not only be organized and processed smoothly, but utilized for greater insight into the business as a whole.

AI offers greater insight into customer data and more seamless data extraction

AI tools can process hundreds or thousands of pages in a fraction of the time it would take a human professional to do the same. Language processing tools allow the AI model to “see” the data and process it from unstructured forms or handwritten notes – saving the human user time and giving greater data insights to the organization as a whole.

Machine learning tools are adept at spotting the patterns that humans might miss. For example, the model may start noticing trends to predict delinquency or fraud. It can provide insights into the customer base or projections for future revenue. It can even flag criteria within your borrower pool that your loan buyers or underwriters can ask about. For example, say the model notices that write offs for BNPL loans are more common among applicants with low credit scores. If the lender is on the fence about a specific buyer’s credit report, they might ask these questions in the dealership before writing the loan.

AI helps lenders process more loans in less time

According to McKinsey research, when AI agents are implemented into the credit department, credit analyst productivity rose by 20-60%. Overall, credit analysts were roughly 30% faster at decisioning a loan. To achieve this speed, an AI agent takes over the document collection, assessment of collateral, authentication of documents, and creditworthiness of the customer. The human employee is then used to review the AI output, have a discussion with the customer, communicate their actions, insights, and next steps, visit the customer’s site or dealership, and present them with the credit offer.

Not only does the approval process move 30% faster, the human employee spends more of their time speaking with the borrower – suggesting that in the future, AI could help banks provide faster service, without losing the human touch.