Nearly Half of Executives Said Deal Jacket Errors Cost Them Approximately $1 Million During 2022
SAN FRANCISCO – (April 4, 2023) – Informed.IQ, developer of AI software that verifies, streamlines and optimizes loan processing, announced an industry survey of auto finance executives. The survey identified key areas where they are leveraging Artificial Intelligence (AI) into their workflows and processes, and the challenges they face implementing automation.
Financial Institutions and auto lenders use AI software to make their operations “smarter,” cheaper and faster. Yet, there are learnings from the following implementation trends and insights. Importantly, the survey, presented to more than 2,500 professionals during March, found approximately one-third said a quarter of their deal jackets had defects in 2022. Today’s new AI-powered software spots common defects during loan origination in VPP products, GAP waivers, or debt cancellation agreements.
You can read the survey here.
Additional Key Highlights from the Survey:
- 95% of lenders are beginning to leverage AI tools in some aspect of their business (i.e. credit decisioning, loan servicing).
- 51% of lenders cited regulator audits as their biggest concern.
- Nearly 44% of lenders are interested in leveraging AI to compete with lenders who are also using these tools.
- 29.7% of lenders said the top metric automation has improved in the lending process is reduced cost.
Auto lenders can mitigate defect scenarios by implementing AI-powered systemic controls that help them avoid audits. Today, most lenders don’t have systemic controls in place to audit the contents of contracts and deal jackets. However, lenders want to implement these controls. They do so by adding staff, difficult in tight labor markets or relying on manual controls, susceptible to human errors. Another option is external vendor partners who provide AI-based software-as-a-service solutions, automating much of the process.
The survey findings showed nearly half of respondents (43%) said deal jacket errors cost them approximately $1 million during 2022.
“Bringing AI into the auto lending process is an important way lenders can stay competitive, profitable and compliant with regulations today,” said Justin Wickett, CEO of Informed.IQ. “Our lender partners rely on our advanced AI technology to simplify and automate the process of collecting and analyzing data, with the goal of helping to fund loans as quickly and efficiently as possible while lowering cost to fund, lowering the cost of processing GAP refunds for early payoffs, improving compliance, and lowering the cost of regulatory matters.
The majority of lenders surveyed said they primarily average between eight and twelve minutes to remediate a deal jacket and then manually onboard it – precious time that adds up when considering thousands of loans in a portfolio. Half of lenders surveyed said they are most concerned about regulator audits, with another 36% saying they’re concerned with the increasing number of staff hours to scrutinize accuracy and/or rectify errors.
Informed uses AI and ML to instantly verify income, assets, residence, insurance, auto stipulations, credit stipulations and more, enabling real-time, reliable credit decisions without bias. Informed’s ML models are trained to process hundreds of document types and consumer-permissioned data sources, automating stipulation clearance for lenders. In 2022, Informed processed over 4 million consumer credit applications for major US lenders, automating over $110 billion in loan originations to date. Informed.IQ automates verifications with 99% accuracy in seconds with no humans-in-the-loop.
Originally focused on auto lending, where six of the top ten auto lenders use the solution, financial institutions now use Informed for mortgages, credit cards, personal and student loans, and automated bank account openings. Founded in 2016, Informed.IQ raised $20M in 2021 from notable investors including Nyca Partners and US Venture Partners. To learn more, visit informediq.com or follow Informed on LinkedIn.