AI in Lending: It's Time to Get Personal | Auto Finance News | Auto Finance News

AI in Lending: It’s Time to Get Personal

More than 250,000 people have asked Alexa, Amazon’s voice-controlled bot, for its hand in marriage, according to a report by New Scientist. It’s safe to say, Artificial Intelligence (AI) has gotten personal.

So, how can lenders use AI to create a more individualized lending experience?

Borrowing 101

Let’s start with product education. Frankly, loan products aren’t top-of-mind for most borrowers until they need a line of credit. Even then, they are typically focused on their current financial need. They want to know: what can I get right now?

Using AI tools, lenders can turn an initial product inquiry into a comprehensive product demonstration. Online, lenders can collect information about potential borrowers’ life goals, spending priorities, timelines and more – and then forecast products and needs across their lifetimes.

Consider this scenario:

Joe is a recent graduate contemplating renting or buying a home or a car. He visits a lender’s website, where an AI agent welcomes him. The chatbot asks Joe about his career and family goals, spending priorities and more.

As Joe enters his data, he can see the impact on his monthly budget, taxes and other finances over a range of years. Joe can also account for changes, like an increase in salary over time.

Using Joe’s personal information – not just what’s included on an application – the lender can recommend a product that would be a good fit for Joe today and forecast how the product would work for him as he gets married, adds a second home or starts a family.

Joe may spend hours “playing” on the lender’s site because he has the freedom and information to plan a very personal financial roadmap. The process is simple and on-demand, and the experience is extremely personal and engaging. But behind the scenes, complex variable mathematical modeling takes place.

Contrast that with the traditional model, in which a loan officer (who may be commission-based) would have to meet with a borrower to gather the same information, and then spend days or weeks developing a range of scenarios. It’s impractical, if not impossible. Using AI, borrowers get the information instantly and can see how different inputs (and products) impact their financial future.

The speed, ease, and personalization of the AI experience help Joe feel both empowered and connected to the lender. For the lender, product education is practically automated because the borrower initiated and managed the learning. As an added benefit, Joe enters the lending process with a firm understanding of his options, responsibilities, and outcomes.

New Borrowers

AI tools can gather, verify and evaluate enormous amounts of data – beyond what is captured on a traditional loan application. The key advantage here is scale: AI can manage more types of data and tremendous amounts of information. With more input, lenders can make better predictions about borrower behavior, and they may be able to confidently say “yes” to more borrowers with less cost and arguably better risk management.

Take first-time borrowers, like Joe from the earlier example. On paper, Joe’s risk seems steep: he has limited or no borrowing history, no long-term income and high amounts of student loan debt. Traditionally, this would make him high-risk for most loan types. So, he could be refused or presented with unappealing loan terms.

But now, using AI, a lender can incorporate other factors, like where a borrower lives and the industry he or she works in, to make a more calculated – but also more personal – lending decision. Accounting for a person’s academic degree, the field of work or metropolitan area, a lender may be able to calculate a lower degree of risk and extend more credit.

The possibilities are nearly endless: rental history is a likely data point, but social media activity or education could be, too. Lenders need to determine which data points are correlative (and not just coincidental) and to make sure they don’t unintentionally insert bias. But as long as data is highly digitally available, AI can execute on it. Fast.

Speed Matters

The ability to incorporate data into a borrower’s profile in microseconds can make all the difference between profitability and lost opportunity. Speed matters in many instances, like in auto lending, where borrowers intend to walk away with a vehicle and financing in a matter of hours. With AI, lenders can quickly extend credit to more borrowers while minimizing risk.

For example, an AI-assisted approval process may recommend a borrower for an auto loan, but at an interest rate or term that is unique to the borrower’s personal situation. Instead of simply denying a borrower, AI can be used to very quickly present individualized terms that protect the lender and are appropriate for the buyer.

Role of the Lender

In many ways, AI extends the role of the lender. Borrowers may begin to view their lender as an advisor or a consultant as they spend more time inputting personal information and playing out scenarios. AI can also become a marketable, distinguishing factor in a highly competitive market.

So, it’s decision time for lenders (unfortunately there’s no AI bot to make this call): do you want to offer borrowers personalized, AI-driven experiences through your brand and your platforms? Or, will you partner with an advisory company that does?

Ignoring AI is not an option. Regardless of your approach, automation and personalization in personal finance will continue to grow. Your best bet is to grow with it. Your competitors certainly will.

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0 thoughts on “AI in Lending: It’s Time to Get Personal

  1. So does this service get the best deal in the customer’s interest or the best deal for the dealer?

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