The lending industry has gone through a period of rapid innovation, with faster, more refined processes becoming achievable due to robust data analytics solutions. However, better risk analytics has spurred expansion in specific sectors where the major technology upgrades were possible.This meant that major large banks capable of taking on the costs of such a change, or niche specialty lenders that could build their entire business around an analytics platform, were the ones able to drive positive change. Many community and regional lenders have been left behind, unable to afford system overhauls and with regulatory and business model pressures that limited their ability to adapt quickly.
Increased accessibility in the risk analytics sector is starting to change this situation. Modern solutions are making data analytics not only more accessible from a price point, but also in terms of integrating analytics into everyday operations. Whether it’s having an analytics system pull information from diverse third-party databases to recommend action on a loan application or automating ID verification, analytics tools can prove invaluable. Solutions that use bank transaction data are poised to take this value to another level, letting firms tap into long-underutilized information that can unlock deeper gains in loan application processing.
How bank transaction data fits into the lending process
Processing a loan application relies on gaining as deep an understanding of the applicant’s financial situation as possible. Historically, that has meant evaluating the applicant’s assets and credit history to identify the potential risk and value of the loan. But there are a few inherent problems and limitations in this strategy, including:
- How to overcome inherent limitations in a traditional credit scoring model.
- How to reach underserved population groups who may have invisible credit.
- How to accelerate risk analysis without getting sloppy and overlooking key factors influencing a decision.
- How to fully understand an applicant’s financial situation, particularly if they lack fixed assets to lend against.
Alternative lending models have worked to overcome many of these challenges. They do this by bringing in data from a wider range of sources and rethinking the credit decisioning process based on that data. But to apply such principles in a bank or credit union setting would require a significant overhaul to not only gain access to third-party databases, but leverage decisioning engines that analyze that information automatically.
The costs of such a transition can be high, and the only way to justify such expenses is to ensure the innovative technology creates sufficient business opportunities to go along with the operational improvements. Risk analytics software that leverages bank transaction data makes such gains possible by letting you seamlessly integrate a loan applicant’s historic banking data into your decisioning process.
For example, imagine you’ve implemented risk analytics tools to help you offer a new lending product focused on smaller consumer loans. On the surface this kind of product may not seem to be particularly valuable for a community bank because you’d need to handle a high volume of loans to justify the cost of handling applicants. But when bank transaction data is automatically accounted for by your credit decisioning engine, you’re able to provide loans for individuals with enough cash flow to handle the debt, even if they don’t have great credit. What’s more, all of the data analysis is automated, delivering recommendations and information behind those recommendations to your loan officers, meaning you can process the loan application, originate funding and deliver the cash quickly and with minimal effort.
With transaction data helping you expand your lending audience and other risk analytics tools informing and automating decisioning, you can offer a wider range of projects to a larger audience, creating sufficient value to justify the inherent costs and disruption of an innovative lending strategy.
Opportunities created by bank transaction data
Offering varied loan products, as highlighted in our example, isn’t the only way bank transaction data can create value. In fact, many of the technology’s core benefits are particularly evident in the actual lending process, something that pays off as more borrowers look for fast turnaround times from loan applications. A few opportunities created by bank transaction data include the ability to:
- Process loan applications faster through quick cash flow analysis that lets you pin down potential risk efficiently.
- Deliver insights directly to employees to save them from manually gathering, sharing and analyzing raw data.
- Provide a full picture into a customer’s relationship with your bank, at a glance, so you can quickly assess the potential benefits and pitfalls of a loan.
- Reduce friction surrounding origination by presenting data-driven evidence that a borrower can afford a loan based on spending and cash flow history.
- Implement fully digital lending processes that reduce clerical work.
A risk analytics platform that takes full advantage of bank transaction data can transform your lending processes for the better. With a blend of advisory and analytics solutions, GDS Link can help you take full advantage of the opportunities on the horizon. Download our whitepaper detailing the full implications of bank transaction data systems to learn more.
About the Author
Carl Spilker, Vice President of Analytics
Carl is an accomplished executive within Global Consumer Lending, boasting over 20 years of industry experience. Carl has a proven track record in delivering consumer lending products and utilizing complex analytical instruments such as AI, Machine Learning and traditional multivariate models.