You are the CEO of a $10 billion community bank, and you just came back from Bank Director’s annual Acquired or Be Acquired Conference in Arizona. While there are still some headwinds such as margin pressure and heightened regulatory scrutiny, you feel optimistic about potential mergers and acquisitions (M&A) activity with the recent surge in bank valuations and the anticipation of lower interest rates.

You feel ready to lead your bank through a period of higher growth. Then you get an email from one of your directors with a link to the McKinsey & Co. article, “Capturing the Full Value of Generative AI in Banking” projecting that the banking industry could bring in $200 billion to $340 billion (equivalent to 9% to 15% of operating profits) through AI-generated productivity. The email includes a simple question: “Are we ready for this?”

Banks are on the cusp of a transformation driven by advancements in predictive AI, generative AI and machine learning (ML). Using these technologies to enhance current operations and bolster fraud protection, banks have the potential to identify efficiencies and offer superior digital banking experiences.

This is particularly true in areas such as enhanced back-office operations, customer acquisition and growth, loan operations, fraud detection and risk management. The challenge involves balancing adopting innovative AI solutions against ethical, compliance and regulatory challenges.

Opportunities and Risks
Generative AI is creating new opportunities to improve call center intelligence, fraud detection and prevention, relationship building, analytics and regulatory compliance. Every day it seems as though there is a new headline about a financial institution deploying generative AI.

“At Santa Cruz, we are exploring AI and Generative AI to enhance our fraud detection capabilities,” says Jaime Manriquez, the executive vice president and chief information officer at California-based Santa Cruz Bank. “These technologies help us mitigate fraudulent transactions, reduce fraud losses, and protect our customers’ data. Ensuring data integrity, accuracy, and security is crucial as we continue to meet regulatory requirements and help our customers combat fraud.”

While generative AI’s impact promises to be transformational, like any new technology, it presents unique risks for banks that include:

  • Model fairness and bias risk
  • Intellectual property infringement
  • Data privacy concerns
  • Security threats
  • Ethical and regulatory compliance
  • Auditing and transparency

“Utilizing generative AI in banking decisions presents opportunity and notable risks,” says Julieann Thurlow, president and CEO of Reading Cooperative Bank in Reading, Massachusetts.

“AI could inadvertently advantage or disadvantage consumers if not managed properly,” adds Thurlow, who is also the current chair of the American Bankers Association. “Ensuring no harm comes from AI decisions is critical, as banks will be accountable from a regulatory perspective. It’s essential to maintain strict control over who holds, uses and handles customer data, ensuring it isn’t exposed or misused. While the rewards of AI are exponential, banks must prioritize data integrity and security to uphold customer trust and comply with regulations.”

Getting Your Bank Ready for Generative AI
As banks continue to implement AI and machine learning, bank leaders should continue to educate themselves on the technologies’ benefits and pitfalls, assess the abilities of their team to adapt to and gain efficiencies from AI, and equip staff with the skills necessary to implement and manage it.

To capture the full promise and value of generative AI, bank executives will need to:

  • Create a strategic road map to address why, when and how to implement generative AI
  • Put together a talent development program
  • Develop operational model and business cases
  • Decide if the bank will use an existing network or build an internal model specifically for the bank
  • Assess and prepare data availability, accessibility and privacy
  • Develop the appropriate risks and controls specific to generative AI

You realize how transformative this technology can be, as long as precautionary measures are in place and you have a well-designed implementation strategy.  You pull out your laptop and get ready to respond to your director.


Cahn Tran

Co-founder & CEO

Canh Tran is the co-founder and CEO of Rippleshot, an AI-based fraud prevention platform that helps over 1,700 financial institutions proactively manage risk and prevent fraud through AI-based rapid risk detection. He and his team continue to push the limits of artificial intelligence to build the next generation of fraud tools to help financial institutions become strategically resilient. He has an MBA from Northwestern and a B.S. from the University of Maryland.


Lisa Gold Schier

Strategic Partnerships

Lisa Gold Schier is a trusted C-Suite advisor to an established, vast network of bankers, technology providers and consultants. She is known for building and leading strategic partnerships from ideation through contract negotiation to bank adoption and ongoing business development. Lisa was a leader at the American Bankers Association, focusing on helping banks and technology providers develop strategies around innovation and technology implementation. She is a frequent speaker on fintech/bank collaboration, providing expertise on how financial institutions and fintechs can successfully work together.