In the competitive auto financing landscape, credit unions emerged as the frontrunners, surpassing traditional banks and financial institutions in 2022. To maintain this lead and capture a larger share of the indirect lending market, credit unions must continue to innovate and leverage cutting-edge technology. A key innovation driving this progress is document processing automation (DPA). By utilizing artificial intelligence (AI) and machine learning, DPA tools can classify, analyze, and extract data from unstructured documents such as pay stubs, W2s, financial records, credit reports, insurance policies, and vehicle service contracts. These advancements enable credit unions to fund loans quickly and consistently, benefiting not only the institutions but also dealerships and members.
Historically, only the largest credit unions could afford the resources to develop customized DPA solutions. However, modern loan origination systems now offer robust integrations with AI-powered tools, making DPA accessible to credit unions of all sizes. This accessibility allows all credit unions to enhance operational efficiencies, strengthen dealer and member relationships, and solidify their position as reliable auto financing sources.
Unlocking efficiency with automated funding
Many credit unions still rely on manual processes for their indirect lending operations. Traditionally, evaluating a loan jacket required employees to meticulously examine and categorize numerous documents to verify the applicant’s income, credit history, and collateral information. This manual approach can be time-consuming, especially for complex or nontraditional applications, leading to delays and potential applicant frustration.
DPA addresses these challenges by using AI to process loan applications with a consistent set of business rules and document naming conventions tailored to each lender’s requirements. This automation not only speeds up processing times but also ensures accuracy and consistency, reducing the need for manual cross-checking.
Transforming indirect lending with DPA
Technological advancements within credit union service organizations (CUSOs) have made DPA capabilities more accessible. According to Informed.IQ, 95% of financial institutions either currently use AI or plan to implement it. Here are the primary benefits of adopting DPA:
- Accelerated loan processing
Today’s consumers expect quick decisions. DPA integrations using AI can significantly reduce funding timelines. These tools are trained to recognize over 250 types of loan jacket documents and more than 45,000 document subtypes, processing them much faster than humans. AI’s fuzzy match logic can identify similarities and patterns in text and data, even with variations, ensuring quick and accurate document processing.
- Improved accuracy
DPA tools are not only fast but also much more accurate. Unlike humans, who may deviate from established processes, AI-powered DPA tools follow predefined rules, ensuring uniform decision-making. AI also excels at detecting forged documents, a critical capability given that income and employment fraud cost auto lenders nearly $5 billion in 2021, according to Informed.IQ.
- Enhanced relationship building
Boosted speed and accuracy in the funding process can lead to better dealer and member interactions. DPA enables faster car closings for buyers and quicker payments for dealers, enhancing the credit union’s reputation as a reliable lender. Additionally, by automating tedious tasks, credit union employees can focus on building personal connections with members and nurturing key dealership relationships.
Leading the way in indirect lending
By embracing DPA technology, credit unions can fund loans quickly and accurately, fostering loyalty among dealers and members. This technological adoption positions credit unions as go-to sources for auto financing and reliable partners committed to exceptional service. Credit unions who leverage DPA stay ahead of the competition, build enduring relationships, and lead the way as a trusted leader in indirect lending.
