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Where Auto Lenders are Already Seeing the Benefits of Artificial Intelligence Outside of ChatGPT

The loan application process has been greatly improved using machine learning (ML) and artificial intelligence (AI) tools. These tools enable auto lenders to make faster decisions in a more accurate manner, which benefits everyone from the dealer, the OEM and certainly the end customer. Lenders can integrate with critical alternative data sources and improve the approval process by using network-wide credit bureau data and neural networks to more accurately predict whether an applicant will be delinquent.

This is especially useful in auto loan underwriting, where alternative data sources can be used to make risk models more predictable. This clearer insight helps lenders take a closer look at a customer’s financial background and make more informed decisions that ultimately create a more inclusive environment for credit lending.

The world of AI goes beyond ChatGPT
However, while these benefits are great for the end customer, there are several key areas where AI is helping lenders specifically. This is important to note, as not all lenders are fully on board with the use of AI and some specific tools, such as ChatGPT.

Chat GPT has taken the internet by storm, once again placing AI technology front and center in people’s minds. And while it is fascinating, Chat GPT has a way to go before becoming a truly valuable and secure business tool. AI software applications, however, have been around for nearly 40 years, providing specialized machine learning models with enough time to train and become indispensable in certain industries, especially financial services.

Financial Institutions and auto lenders have been leveraging AI software to make their operations “smarter,” and more efficient for the last decade, uncovering countless benefits such as funding dealers faster, avoiding charge-offs, automating second-line risk controls, reducing friction, freeing up staff.

AI implementation is a partnership between new technology and existing legacy platforms
But in order to realize these benefits, lenders need a partner with a record of interacting with legacy systems. A recent lender survey showed how 37 percent remain resistant to change in using AI technology. Change management requires planning, expertise and resources1. Lenders prefer an agile approach to success where they demonstrate wins along the way to counter the resistance to change.

This also means lenders are scrutinizing every detail in finding AI partners to help move off old systems while implementing intelligent automation. Smart lenders are asking the right questions, such as: Do you have professional services or customer success people to help? How complex is the integration with our in-house systems? And, what changes to our processes and systems is required?

AI is being used to fight fraud
Once the right partner is found, it’s not long before lenders realize specific benefits. As one example, fraud rates are rising along with increased use of digital tools. And fraud was the top challenge cited in the survey when it comes to documentation defects according to 55 percent of respondents. Closely behind at 54 percent was “employer name mismatch,” one of the easiest defects to remediate with properly trained AI/ML solutions¹.

To prevent fraud in auto lending, lenders are now relying on AI-driven automation tools to implement strong fraud detection and prevention processes, including verifying borrower information, conducting background checks on dealerships, and monitoring loan performance. It is also important for lenders to provide fraud prevention education and training to employees to help them identify and prevent fraudulent activities.

Automation software can reduce errors
As a second example, identifying and rectifying errors in deal jackets remains another top issue. About 66 percent of respondents reported that at least a quarter of their deal jackets had defects in 2022, and those defects cost between $1M and $5M in associated costs. Auto lenders can more stringently mitigate defect scenarios by implementing AI-powered systemic controls that help them avoid audits. Today, the vast majority of lenders still do not have systemic controls in place to audit the contents of contracts and deal jackets¹.

However, lenders are looking at implementing these controls either through added in-house staff, which is difficult in today’s tight labor market; relying on existing manual controls, which are susceptible to human oversight or errors; or through a trusted partner to provide AI-based solutions to automate much of the process.

Minimizing these errors makes a drastic difference to a lender’s operations. Regulator audits are top of mind when considering accuracy of deal jackets – in fact, 51 percent of auto lenders cited regulator audits as their biggest concern. AI technology combined with intelligent automation streamlines audits and bolsters compliance through programmatic and documented controls in an ever-evolving regulatory environment¹.

All of this isn’t to say lenders are driving their portfolio strategies through fear. The large majority are in fact doing the opposite – looking to navigate the current business climate challenges while also identifying technologies that can provide a distinct competitive edge. The key is in finding the right AI technology that builds trust, reduces errors, improves accuracy, enhances risk management, and offers a clear return on their investment. With all of this in place, lenders will be ready to scale for the next up-cycle.

Jessica Gonzalez
Jessica Gonzalez
Jessica Gonzalez is Director of Lending Strategies for, an AI startup serving the financial services industry that uses machine learning models to classify, analyze, and extract data from documents used in consumer lending, mortgage, and bank account openings. For more information, please email [email protected].
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