How Lenders Can Put AI Software and Big Data Into Action

AI software will help lenders take action on big data.

When it comes to Big Data and lending, we know lenders have plenty of data. But here’s the big question: Are they getting the most out of it? Are they acting on it? We believe AI software will help.

My company has helped lending clients act on AI at all steps of the lending process — to prospect and reach new leads, reduce time spent assessing loan applications and monitor the portfolio of borrowers after loans have been made. Along the way, AI can actually make lending more human.

AI software, like LIFT, which I created to help lenders increase loan volume and reduce risk, can help lenders at every stage of the lending process. By automating standardized data collection and analysis, AI tools can free up time for sales people, underwriters and relationship managers to focus on personalized aspects of prospecting, lending and monitoring.

Here are some examples of how AI software can help lenders across the process:

Prospect and reach new leads

Finding new customers is top of mind for all lenders. Some of these clients already know who is in their customer base. AI tools can bring that data together with third-party sources and other economic information to predict the ideal candidates for loan products.

Do these potential customers need capital to expand? What do their credit histories, industries and portfolios tell us about their risk profiles? AI can automate the answers to these questions, and customize matches, for both traditional banks and nonbank lenders.

It’s one thing to find leads, but AI can help you reach those ideal candidates, too. Enter AI tools, which marketing and communications teams can use to slice and dice their customer data, analyze data from A/B testing and automate the generation of customized surveys and lists. This helps refine and customize sales and marketing messaging to generate repeat and new business.

Assess loan applications

Once a prospect becomes a customer, lenders can spend lots of time analyzing and assessing their application. AI apps can make this process easier for the lender and open new opportunities for borrowers who in the past might have been overlooked.

For every loan application, there are three types of potential borrowers – clearly good risks, clearly bad risks and applicants somewhere in the middle. What makes a good or bad risk will depend on the lender’s priorities and risk tolerance. AI software, like LIFT, score these applications by comparing them to trends and patterns in the lender’s historical loan decisions. AI algorithms then automatically recommend approving or denying those borrowers on either extreme.

Traditional lenders might choose to automate the process entirely using the LIFT score. Others use the time saved from reviewing clearly good or clearly bad risks to focus on opportunities in the middle of the applicant pool.

Take our client, LiftFund, for example. (No connection to our LIFT, by the way.) This nonprofit lends to small business owners who don’t have access to commercial credit. Some borrowers’ businesses are so new they might have limited cash flow to qualify. But lenders can often discover more resources to help the borrower qualify by digging deeper into the borrower’s profile. We created an end-to-end credit scoring system that freed up LiftFund underwriters’ time to focus on potential borrowers with resources that scoring systems might not consider when making a lending decision.

Monitor the portfolio of borrowers

To ensure repayment, bank and nonbank lenders have always monitored economic trends, industry outlooks, and borrowers’ credit and financial data. In the best cases, this work identified potential repayment problems shortly before they happened.

Today, AI software is helping our clients simultaneously analyze real-time economic data and borrowers’ past behavior to identify signs of risk up to 90 days early. These AI models can predict risk with greater accuracy than traditional financial ratios and benchmarks. Instead of waiting for a missed payment or broken covenant, lenders can use these early warning systems to act before default.

Relationship managers can reach out to at-risk borrowers to assess the risk in greater detail or work out alternative payment schedules, trade credit or other arrangements to avoid default. Internally, lenders can downgrade loans’ risk profiles and freeze or reduce credit lines to avoid additional debt.

From prospecting to repayment, AI software is revolutionizing how lenders and borrowers use credit. These are just a few of the ways lenders can act on AI and that AI can make lending more human.

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