4 minutes
Leverage AI-driven tools to make the best use of the deposit influx and marketing resources.
Today, financial institutions are awash in deposits from pandemic-era stimulus programs, but they can take steps to balance this influx with lending. It’s time to embrace cross-selling predictive modeling to more accurately and proactively identify the unsatisfied needs of members and prospective borrowers.
Adopting the power of AI predictive analytics as part of an overall data strategy is an important consideration for financial institutions as banking shifts deeper into a digital-first industry. In fact, 88% of professionals using AI agree it impacts their companies’ competitive advantage. Credit unions that use traditional methods of targeting audience selection, or no targeting at all, won’t be able to keep pace with competitors that continually leverage the data at their disposal.
A Win-Win for Institutions and Members
Bank deposits have surged this year. Just in Q2 2021, they rose by $411 billion to $17.09 trillion, according to the latest available data from the Federal Reserve. To put it in perspective, this is still slower than spring 2020 when banks were inundated with deposits from early federal relief programs, but it’s nearly four times the average level of deposits over the past 20 years, according to the Fed data.
In most cases, deposits acquisition are an ongoing initiative for financial institutions and incur favorable results—but only if they can use this money to make loans. With slow loan adoption, profitability is affected.
Credit unions should focus on cross-selling products such as HELOCs, HELOANS, mortgages, auto loans and more to those members with high deposit balances or increased payroll deposits. Pivoting your marketing strategy to offer these products will help combat the likely imbalance in your organization’s deposit to loan ratio. But identifying and marketing to the right audience continues to be a challenge as the industry wraps up 2021 and heads into the new year. Without predictive modeling, marketing efforts have the effect of a firehose. Predictive modeling can focus that effort into relevant, targeted campaigns and make “firehose” market saturation a thing of the past. And don’t forget small businesses—their needs are market-driven too and can benefit from a relevant cross sell just as much as financial institutions.
Put Your Data to Work
When cross-selling models are implemented using predictive analytics, credit unions can generate new business from existing members, lowering their marketing expenses. Members are offered timely and relevant lending products when they’re most likely to need them and adopt them.
Predictive models work by aggregating product usage history and member demographics to learn what adopters of products and services look like and how they behave. The longer the predictive model is live, the better it becomes at identifying the proper audience. A best-in-class data partner will add even more strength to the model by layering in a robust understanding of spending behaviors and patterns to further fuel predictions. From there, a credit union can determine which of its members is most likely to need a lending product. Reaching the right audience at the right time with the right offer is key to growing lending business and can make a huge impact, especially on smaller institutions. It also decreases member attrition. Most FIs learn that they’ve lost a client only after the final decision has been made and it’s too late to make a difference. However, those that capitalize on their data can spot signs of trouble before the member decides to exit, when they still have a good shot at intervening and
retaining them.
AI Is in Reach
On Celent’s recent “Technology Trends Previsory” webinar, it was presented that research around the three-year evolution of AI adoption in banking has shown incredible growth in positively impactful use cases. Celent analysts said that such technology as AI should be the foundation for all transformation efforts in the coming year.
AI-driven predictive models have, traditionally, taken months to deploy and are typically expensive, which means that in the past only the largest financial institutions were able to benefit from them. As a result, smaller FIs have been at a major competitive disadvantage in using predictive analytics to drive business decisions. But affordable predictive modeling solutions are on the rise—closing the competitive advantage gap for institutions that take initiative to leverage their existing data. AI and machine learning are not just tantalizing buzzwords—these are real technologies that have the capability to provide businesses with incredible insights. Of course, the output of these predictive tools is only as good as the data being analyzed. Good data science and predictive modeling relies on cleansed and contextualized data.
It’s important to work with the right technology partner to start benefiting from cross-selling predictive models. An industry-leading partner should ensure that cross-sell campaigns are updated daily, always on and execute offers in front of the right people at the right time, maximizing the potential ROI. Ensure that your budget for 2022 includes spend within your technology and marketing ecosystem to cross-sell products intelligently.
Nate Shahan is the co-founder and chief product officer at Segmint, the world leader in making transaction data meaningful and actionable for financial institutions. Shahan co-founded Segmint Inc. in 2007, and since then has had a continued focus on helping financial institutions of all sizes better understand and engage with their customers. He currently holds the position of chief product officer for Segmint, driving product strategy, innovation, and roadmap planning.
