Improving lending profitability and fairness with AI

Lenders spend countless hours manually verifying loan applicants’ information — specifically, income, residence, employment and proof of insurance. In addition to verifying consumer information, there is ample work required to review deal jackets and corresponding ancillary product contracts, such as vehicle service contracts and GAP.  

A big pain point for lenders is handling ancillary product contracts. In the U.S., there are more than 40,000 variations of ancillary product contract form numbers and revision dates, and up to 200 different variations on a state-by-state basis for document types such as retail installment sales contracts. This creates confusion and leads to funding errors; varying formats of paystubs, bank statements and insurance documents add to the headache. 

This manual process leads to long funding timelines, resulting in held offerings and contracts in transit, causing an undesirable customer experience, dealer dissatisfaction and high operating expenses for lenders. Leading lenders are turning to technology to speed funding times and improve profitability. 

Addressing security risks 

At the same time, fraudulent pay stubs are on the rise as digital retailing and online applications proliferate, and these are difficult to detect with the human eye. Lastly, income calculation is complex, and humans make mistakes — the more manual processes in place, the greater compliance and fair lending risk.  

Artificial Intelligence (AI) technology can be used to extract and classify documents, programmatically calculate income in accordance with a lender’s policies, flag fraudulent documents, and identify defects in deal jackets to automate many checks otherwise done manually and get answers back to dealers and customers much more quickly. 

AI and machine learning tools automate the reviews of these documents, automatically calculate income and detect fraud. This ultimately enables lenders to increase profitability, fund loans faster, reduce operating expenses or create efficiency gains, mitigate fraud, and enable employees to further focus on what matters most — building deep relationships with their customers. 

AI can reduce bias in lending 

In addition to increasing profits, AI also increases fairness and financial inclusivity by reducing bias. The key to reducing the effect of bias is to understand the consumer AI process. There are three major elements: 

• The expansion of data available for decision-making; 
• The models that detect relationships in data; and 
• The automation of decision-making based on model predictions of loan profitability.  

There is no shortage of data to use when building models, and much of this data is publicly available. The key is using the right data in the right way. Otherwise, you risk building bias into the models. 

Many financial institutions are turning to AI to reverse past discrimination in lending and to foster a more inclusive economy. The Consumer Financial Protection Bureau is monitoring the situation to ensure this is the case, making sure that the models aren’t as biased, or even more biased, than humans. It is incumbent on those building the software systems to ensure this is the case. 

Adine Deford is vice president of marketing at Informed.IQ. She has more than 25 years of technology marketing experience serving industry leaders, world-class marketing agencies and technology startups.