Digital banking has distanced customers from their banks, eroding personal connections once forged in branches. This gap is costing banks billions in lost opportunities and weakened relationships.
But AI could reignite connections between banks and their customers. Micronotes AI is helping banks understand customer needs and offer helpful information based on their actions or situation, creating new opportunities for relationship building.
In the latest episode of The Disruption Is Now, host Greg Matusky sits down with Devon Kinkead, founder and CEO of Micronotes AI, to discuss the best opportunities for implementing AI in banking. They explore why not every problem requires machine learning, the importance of understanding AI tools, and how targeted AI applications can improve customer engagement and trust.
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Key takeaways
Banks are losing touch with customers
As banking services shift online, the personal connections that once existed between banks and their customers are fading. Devon pointed out that younger generations don’t even understand the concept of a bank branch.
This disconnection leads to missed opportunities and weakened relationships. For example, Greg shared how he moved large sums of money between banks during the pandemic for FDIC insurance purposes. Despite these significant transactions, none of the banks reached out to offer guidance or solutions, such as informing him about ways to increase his insured limits.
Misuse of AI can lead to regulatory trouble
Implementing AI without a thorough understanding of how it works can result in serious legal issues. The banking industry operates under strict regulations, and careless use of AI can inadvertently lead to discriminatory practices like redlining.
Banks using demographic data for digital marketing might unintentionally exclude disadvantaged communities, attracting scrutiny from the Department of Justice and resulting in hefty fines. The DOJ has increased its efforts to identify such practices by analyzing zip codes and demographics rather than relying on consumer complaints. This underscores the importance of using AI responsibly and ensuring compliance with all regulatory standards.
Not every problem needs machine learning
While AI and machine learning offer powerful capabilities, they aren’t always the optimal solution. Devon emphasized that simpler statistical methods can be more effective and less risky in certain scenarios.
For instance, banks can detect a sudden influx of funds into a customer’s account using straightforward statistical analysis. By monitoring account activity, banks can identify when a customer’s average balance significantly increases and proactively engage with them.
This approach doesn’t require complex machine learning models and avoids unnecessary complexity while still allowing for a quick response.
Education is key for AI adoption in banking
There’s a significant knowledge gap among bankers when it comes to understanding AI and machine learning. Devon shares his experiences conducting educational sessions for banking professionals, highlighting that many lack familiarity with basic AI terminology and concepts.
For example, they may not understand what constitutes relevant data for making accurate predictions or how to evaluate the performance of different AI models. He uses the analogy of predicting water flow from a faucet, explaining that irrelevant data, like the faucet’s orientation, won’t lead to accurate predictions.
Without foundational knowledge, bankers struggle to make informed decisions about AI solutions, potentially leading to ineffective implementations and missed opportunities.
AI can reconnect banks with their customers
Micronotes uses AI to initiate meaningful conversations between banks and their customers, bridging the communication gap created by digital banking. By analyzing customer data, they can identify significant events and proactively engage with the customer to offer relevant services.
Devon mentions that their AI-driven interviews on mobile banking platforms help banks understand their customers’ lives and needs. In addition, Micronotes uses AI to predict customer attrition with high accuracy, allowing banks to address issues before losing valuable clients. This targeted use of AI enhances customer satisfaction and strengthens relationships, ultimately benefiting both the customer and the bank’s bottom line.
Key moments
- AI is not the solution to everything (1:38)
- Digital banking has eroded personal connections between banks and customers (3:25)
- Dangers of regulatory issues (5:25)
- Choosing between machine learning and statistical methods (9:52)
- Customer points of pain with banks (12:57)
- Why exceptional deposits are not a good machine learning use case (14:15)
- The AI education gap in banking (17:15)
- Examples of AI opportunities, limitations, and perceived limitations in PR (21:17)
- Dichotomy in AI use in banking and wealth management (25:16)
- Micronotes’ future plans to improve bank-customer relationships (26:45)
- How AI helps bankers lower borrowing costs for customers (29:44)
