What AI and machine learning mean for auto lending technology

Automotive lenders find themselves in a more competitive market than ever. Lingering supply chain issues generated by the COVID-19 pandemic are being further impacted by hurricane conditions. Yet, challenges tend to bring about new ways to generate efficiency that drive the marketplace to adopt emerging technologies that can increase productivity and optimize workflows.

Many industries, including automotive finance, are turning to artificial intelligence (AI) and machine learning (ML) for these efficiencies. The technologies can deliver a competitive edge to lenders looking to not just survive, but thrive, in today’s uber-competitive marketplace. As demand continues to escalate, lenders need faster and more reliable ways to assess risks, underwrite applications, file and store documentation, verify income levels and book deals. By expediting all these processes and delivering better predictive models, lenders can close deals more quickly while reducing risks.

First, to define and differentiate between AI and ML. Both can be used to help with automating the procedures that comprise the overall lending process. AI more often refers to the ability for a system — such as a loan origination (LOS) or loan management system (LMS) — to emulate human behavior. ML, as a subset of AI, takes these intelligent features a step further, leveraging historical data to automatically predict or learn patterns, and forecast behaviors and outcomes.

But what can auto finance technologies automate, and just as importantly, what can’t they do? What functions are most applicable to AI and ML capabilities? And what impact will these disciplines have on the future of automotive lending? Lenders should know where AI and ML are seeing wider adoption in automotive lending and how they support their objectives in categories like those below:

Document management: AI-powered capabilities such as optical character recognition (OCR) are frequently used in a document management capacity that allow lenders to automatically upload forms, recognize digitized signatures, and conduct document indexing and sorting. Such features can improve manual sort time per transaction and increase accuracy by reducing opportunities for human error. This is significant for lenders processing thousands of loans at any given time. OCR can also support meta data validation, isolating the data elements required for funding.

Auto-Decisioning: The underwriting process is designed to accurately assess risk levels and other criteria to reach an approval decision. ML involves the analysis of historical data to establish a predictive model, typically based on factors such as credit history and vehicle specifics, in addition to details supplied by the applicant. Robust AI systems can create an auto-decisioning matrix or credit scorecard based on a variety of data sources, allowing lenders to assign these risk levels more accurately and consistently deduce the borrower’s ability to repay. As time progresses and a lending system gathers a wider breadth of data, the technology will be able to generate more accurate predictions than conventional, manual, rules-based underwriting processes.

More accurate predictive and behavioral models: ML can help agents manage the borrower’s account over the life of the loan, creating efficiencies that make it easier to service. For example, automated systems can learn what time of day is best to contact an individual borrower based on historical behaviors and propensities. This reduces the typically lengthy, repetitive calls and email trails circulating between all parties to create more effective communications and ensure loan requirements are met.

Loan servicing: By analyzing a broad base of historical payment data, these predictive models can be leveraged to estimate which borrowers are most likely to fall behind in their payments. Skipped payments, inconsistencies in schedules and insufficient funds are warning signs that are better addressed if identified earlier. Clients who are flagged to potentially miss a payment can be assigned more attentive outreach to help keep them on track.

Streamlined workflows: Automation, AI and ML can drastically reduce manual processing through a paperless environment. But more than that, predictive ML algorithms establish what’s known as “Next Best Action” (NBA) recommendations, which help loan officers make more informed choices in managing their accounts. These insights help to improve workflows, plan tasks and generate better outcomes with fewer delinquencies.

Automation, AI and ML will continue to play a greater role in tasks ranging from automatic submission of deals for funding and income verification to predicting the future needs of customers to provide more bespoke services. Reports like NVIDIA’s “State of AI in Financial Services” study predict that future AI-enabled applications will address areas like customer acquisition and retention as well as the cross-selling of personalized financial products and services — a feature that robust LOS technologies in the auto finance community have already begun to adopt.

As AI and ML mature, the technology will play a more prominent role in automotive lending. Competitive conditions in the market will only help accelerate its adoption industrywide, particularly since lenders will require faster decisioning, more accurate predictive models and increased productivity to succeed.

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