This blog post was originally published in September 2024 and was republished December 10, 2024 with updated research.
Artificial intelligence (AI) in banking is becoming a driving force in financial services. According to Alkami’s market study, “The Application and Consumer Perception of Artificial Intelligence in Banking,” 96% of financial institutions believe that AI will play a critical role in the industry within the next five years. Interestingly, 61% of consumers agree that AI will significantly influence their banking experiences. As financial institutions prepare for this future, integrating predictive AI into their strategies is essential for growth.
Before you read on, if you have not read the first blog in this series, Gain a Competitive Edge through Data Analytics in Banking, hop over there first to gain a foundational understanding of the power of data analytics in banking, and then come back here.
Predictive AI offers financial institutions the ability to analyze massive amounts of transaction data, identify patterns, and predict future behaviors. Alkami’s predictive AI provides:
Financial institutions can use predictive AI to enhance the account holder experience, improve cross-selling efforts, and ultimately increase growth and revenue.
Predictive AI models highlight specific account holder behaviors that indicate the potential for cross-selling. By leveraging data tags built from predictive AI models, financial institutions can launch highly targeted marketing campaigns that resonate with the right account holders at the right time.
Below are a few examples of cross-sell data tags derived from predictive AI modeling, and how financial institutions can use them to boost results:
AI CD Cross-Sell Data Tag
What it means: The AI CD Cross-Sell data tag identifies account holders who are likely to be interested in opening a Certificate of Deposit (CD). This could be based on behaviors such as consistent savings deposits, low transaction volumes, or risk-averse investment patterns.
Marketing campaign idea: A campaign targeting these account holders could highlight the security and guaranteed returns of CDs. Using personalized messaging, financial institutions can position CDs as a safe option in uncertain economic times, or as a tool for retirement planning. For example, a targeted email could promote a “limited-time higher rate” offer, showing exactly how much the account holder stands to earn based on their current savings balance.
AI HELOC Cross-Sell Data Tag
What it means: The AI HELOC Cross-Sell data tag spots account holders who may be interested in a home equity line of credit (HELOC). These individuals may have considerable home equity and a pattern of significant home improvement spending or large, irregular payments that suggest they are looking for flexible borrowing options.
Marketing campaign idea: A financial institution could run a campaign offering a special introductory rate on HELOCs, with messaging focused on home improvement or debt consolidation. Financial institutions could also integrate these campaigns with personalized financial health check-ups, offering tailored advice on how a HELOC could help meet long-term goals like home renovations or major life events.
AI Personal Loan Cross-Sell Data Tag
What it means: This data tag detects account holders who are likely to be in the market for a personal loan. Indicators might include large credit card balances, irregular spending habits, or recent changes in income or expenses.
Marketing campaign idea: A campaign targeting these account holders might focus on the ease and convenience of consolidating credit card debt through a low-interest personal loan. Financial institutions could emphasize the simplicity of the process, the speed of fund access, and the potential savings on interest payments over time. A well-timed email or digital banking notification could offer a pre-approved loan with a customized repayment plan, tailored to the account holder’s current financial situation.
Capital Credit Union leveraged Alkami’s AI predictive models to drive retention and growth through targeted marketing campaigns. In a six-month trial, they increased auto loan acquisition by $14.7 million and identified additional home equity loan prospects worth $2.6 million using AI models. The AI technology helped capture prospects missed through traditional methods, acting as a “safety net” for identifying high-potential members.
NET Federal Credit Union scaled their cross-sell capabilities using Alkami’s financial services marketing solutions. By harnessing data insights and predictive AI, they launched personalized marketing campaigns that resulted in increased product adoption and engagement. The AI-powered models allowed them to identify new growth opportunities and optimize member engagement strategies.
The value of predictive AI lies in its ability to provide actionable insights that align with both account holder needs and business goals. Predictive AI models built specifically for the financial industry not only enhance cross-selling because of the highly relevant targeting, but also help institutions remain compliant with regulatory standards, reducing risk. Additionally, these models continuously evolve by analyzing daily transactions, enabling banks and credit unions to stay ahead of trends and adapt their strategies in real-time.
The next five years will be critical for banks and credit unions as they experiment with integrating AI into their digital strategies. The institutions that embrace predictive AI early will likely see the greatest success.
