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Artificial Intelligence Reaches the Banking Mainstream

After many years as a “science project,” Artificial Intelligence (AI) is delivering real value for financial institutions of all sizes.

AI has long been a hot topic in financial services circles, for a variety of reasons. Banking activity generates reams of transactional data, precisely the type of information needed to power AI engines. Banking clients-both consumers and small businesses-crave the additional insights and advice such data analysis can generate. And financial institutions of all sizes- not to mention non-bank market disruptors- are keen to generate added value and loyalty by delivering these insights.

As recently as a few years ago, such AI initiatives were relegated to lab projects with data scientists toiling away far removed from day-to-day operations, aiming to prove the concept’s potential. Only the largest national banks had the R&D budgets to afford such projects, investing in hopes of creating a “first mover advantage” and a leg up over their competitors.

Several of these experiments panned out-leading to high-profile products and features like Bank of America’s Erica, Capital One’s Eno, and Wells Fargo’s Control Tower. Given the modern technology curve, however, AI is no longer the exclusive domain of the megabanks. With the help of fintech partners, community banks and credit unions are already harnessing the technology to generate immediate ROI with both existing and new customers.

Predictive modeling, a common application of AI, has many uses within a bank setting. Credit scoring is perhaps the best known and most established use of predictive modeling-after all, its entire purpose is to determine the likelihood that a loan will be repaid. AI has proven quite effective in identifying previously unnoticed patterns in repayment behavior, bringing greater sophistication and accuracy to these models.

AI has also enhanced the effectiveness of front-line associates, who can now be prompted with better matched product recommendations for prospective clients, as well as more rapidly retrieve relevant data to resolve queries. Customer experience for inbound contact center support has also been enhanced by AI, which powers chatbots to replace conventional IVRs in a variety of self-service scenarios. These capabilities can be delivered through either voice recognition or text, offering a more satisfying- and surprisingly robust- alternative to the stereotypical frustrating maze of IVR options. Most importantly, thanks to advancements in API technology many of these solutions can be implemented in a matter of weeks rather than months, generating immediate results.

AI can also be used in optimizing financial institutions’ marketing campaigns. When AI-based predictive modeling is included in Digital Experience Platforms, financial institutions can run many more-and meaningfully more effective- marketing campaigns without increasing staff. Legacy rules-based programs, the likes of which have been used by banks and credit unions for decades, require detailed oversight of each individual campaign.  A team member must create the campaign, establish rules for the desired targeting of prospects, monitor the results and if possible, course correct when appropriate. Unless a bank has a data scientist on the payroll, designing a high-performing rules- based formula is often the most onerous of these tasks.

Most financial institutions employ multiple methods of targeting prospects. Often, they create rules to isolate specific products and groups of consumers- for instance based on age, income, zip code or recent life event (relocation, new baby, graduation, etc.) The variety of data points available, from both internal and external sources, is virtually limitless. Harmonizing these options can pose an insurmountable hurdle for legacy models, however, given the need to convert them into an actionable target list, and ultimately measure the actual results.

With predictive modeling, the AI engine determines which characteristics are most correlated with the desired behavior- usually propensity to buy a given product. The engine optimizes across the countless permutations of inputs, monitoring results and adjusting formulas when needed. Thanks to machine learning, future campaigns become more effective as well.  And the FI’s marketing team is freed to focus on value-added tasks like messaging and outreach rather than the technical nuances of demand generation.

These same capabilities can be used in solutions that offer opportunities to have an even deeper engagement with the mobile banking channel via an Instagram-like experience. Younger demographics- a key bank and credit union growth segment- have demonstrated a clear preference for interacting with this type of visual content, and with concise packets of information.

Providing financial institutions with the ability to tailor a “Spotlight Story” to each client, serving up an array of customized offers, news, financial advice, etc., through the mobile banking app, further strengthens the customer experience. Content can be hyper-localized (promoting area blood drives or neighborhood businesses, for instance) and constantly updated for immediate relevance and to further appeal to each client’s demonstrated preferences.

Too often, AI is mistakenly equated with the notion of robots taking away jobs from humans. AI powered solutions are most valuable when they augment rather than replace existing staff. Marketing budgets are already tightly stretched, and financial institutions have long been challenged to generate demand across a variety of lending and investment products- as well as deepen relationships. When deployed properly, AI can be the long-sought answer to achieving these goals without adding to existing staff.

AI is revolutionizing business practices across multiple areas of community financial institutions. This once esoteric technology is delivering real results, enhancing the ability to respond to members in real-time, speeding customer service, and opening avenues to marketing in a respectful, personalized manner. Fintech companies have made the technology affordable for credit unions and community banks and have greatly simplified and shortened the implementation requirements. AI is no longer the exclusive domain of the megabanks- a breakthrough that should benefit consumers, small business and community institutions alike.

About Author
Jill Homan is president of DeepTarget, a FinTech company developing and deploying an open, data-driven customer engagement and cross-selling platform for credit unions and banks.

This content is for CU BUSINESS eMagazine , THE TEAM BUILDER (GROUP SUBSCRIPTION), and Special Deal: 2 websites members only.
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