Loan approval relies heavily on two questions. First, does the member intend to pay? Second, is the member able to pay? Successful lenders are able to correctly determine the answers to both of these questions. Not too long ago, lenders depended on limited data, which was essentially a collection of standards and procedures. However, now they have endless amounts of data at their disposal, from website data to transaction data and everything in between. The best part is that your credit union can use all of these data sets to make informed lending decisions.
With the help of modern data analytics tools, you can better understand the data you obtain and use it to take care of your members' financial needs while also padding your CU's bottom line. Let's take a look at several advantages data analytics can provide for lenders:
The goal of lending is to match the best services to the right members. Data analytics can determine a member's financial behavior and spending patterns, and predict their affinity for specific services. With a better-segmented member base, credit unions can more effectively target their marketing efforts.
Each borrower is different, which means their loan will require a different interest rate and amount. Thanks to data analytics, lenders can develop real-time personalized loans for each member. Doing so can help CUs improve conversion rates, as the loans will closely fit each member's individual lending needs.
There are occasions when borrowers who seem to have a perfect payment record start becoming inconsistent after their loan is sanctioned. Although this can be extremely hard to anticipate early on and can put CUs at risk, delinquency prediction models use data to reduce that risk and help credit unions take remedial action quicker. A few examples of the data are past loans, transaction history, and the number of times an individual has made a late payment.
Credit unions have the opportunity to use data analytics to get a more in-depth look at members' behaviors and attributes, which can help maximize collections. Traditionally speaking, lenders would separate members into different risk groups and create different communication approaches for each of them. However, with data analytics, CUs can discover much more about their members, from demographics and risk ratings to account activity and collections, and categorize them into micro-segments.
Credit fraud is one of the most prevalent issues for lenders. Fortunately, data from a variety of sources (social media, mobile data usage, etc.) can confirm a member's identity and prevent fraud before it becomes an issue.
With machine learning, credit unions can automate any step in the loan process, from customer segmentation to loan monitoring. As a result, CUs can benefit from significant time and cost savings that will allow employees to focus on other more pressing needs and help reduce budgetary strain.
Embedding data analytics into your credit union's lending process can help you understand your members on a deeper level, enabling you to better fulfill their financial needs. And not only that, but it can also save you valuable time and money (which we all need more of these days). The most successful CUs will embrace this advanced technology and use it to make the most out of every piece of data they acquire. From automation and fraud detection to personalized loan offers and member segmentation, data analytics is a highly-profitable tool for credit unions. In today's competitive climate, you can't afford to not jump on the data analytics bandwagon.
Are you ready to learn more about data analytics? Our partnership with SavvyMoney features targeted lending campaigns, pre-qualified loan offers, loan lead identification, and timely alerts that notify you when a lending opportunity has been detected. If you're interested in learning more about this integration, we invite you to download our FLEX & SavvyMoney Integration eGuide.