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57 questions financial institutions should ask about AI


April 15, 2024 | Originally published on Bank Automation News | By Whitney McDonald

AI continues to take the financial services industry by storm, but as the mega-big banks implement internal AI councils, hire chief data officers and invest heavily in the tech, many institutions are still asking “Where do we start our AI journey?” 

Within banking, 87% of bank decision-makers said that there is increasing pressure from boards and leadership teams to implement AI within their organizations, according to a February NTT DATA report that surveyed 650 global banking decision-makers. Tokyo-based NTT DATA is a global digital business and IT services provider. 

But, when approaching AI projects, there’s no one-size-fits-all approach and leaders at financial institutions seem to have more questions than answers.  

“I don’t think there’s necessarily a right or wrong [approach to AI], as long as it makes sense and has some correlation back to your strategy,” Michael Lehmbeck, chief technology officer at BankUnited, said last month at Bank Automation Summit U.S. 2024 in Nashville, Tenn. 

Most financial institutions are considering AI use cases, and there are lessons to be learned in how they are approaching the technology. 

Bank Automation News asked financial leaders: What questions are you asking yourself and your institution when considering AI implementation? 

This is what they had to say: 

  1. What is real and what is hype today with AI? 
  2. Where should we start? 
  3. How do we use AI to accentuate our human service, rather than pointing customers to a bot? 
  4. What business problem are you solving? 
  5. What is our goal with AI? 
  6. How can we use AI to enable more personalized customer banking experiences? 
  7. What KPIs can we draw from AI, and how should we measure them? 
  8. How can we ensure customer privacy when using AI? 
  9. What’s the right approach to writing an AI policy? 
  10. Will regulators allow us to use AI in products at our bank? 
  11. How do we quantify the benefits of a technology that uses AI for our bank? 
  12. How do we justify the additional spend for AI applications? 
  13. Will AI replace our bankers or make them more effective? 
  14. Who in the FI should be involved in AI governance? 
  15. What does safe, secure and transparent AI for sensitive banking functions (such as compliance) look like? 
  16. What is being done by AI companies to overcome problems posed by “black box” decision-making? 
  17. What are the implications for banks looking to utilize AI products, both on the customer side and in the back office? 
  18. What differentiates AI products capable of handling sensitive financial information from other AI products?  
  19. How will AI help our consumers from a financial wellness perspective?  
  20. Will there ever be an “all AI” solution?  
  21. Are there uses of unsupervised AI within the bank (e.g., anti-money laundering)? 
  22. How can we make sure that our data is in a place that is safe/feasible for AI use?  
  23. What are tactical ways we can use AI to enhance customer satisfaction? 
  24. What are ways we can implement AI to help reduce costs and improve efficiencies?  
  25. How can we ensure that use of AI does not result in inadvertent bias? 
  26. When should we use traditional AI vs. generative AI? 
  27. Who are the players (fintechs and banks) developing disruptive capabilities using AI that banks should monitor or look to partner with? 
  28. What strategic purpose does our AI implementation serve and how does it align with our overarching business objectives? 
  29. Will AI save time, increase accuracy or speed up processes? 
  30. How does the adoption of AI translate into tangible business value, whether through cost savings, revenue generation or improved customer experiences? 
  31. Is this a deterministic or probabilistic use of AI, and how does this influence the reliability and predictability of our AI-driven solutions? 
  32. What data will be used to train the AI models and what measures are in place to ensure data quality, integrity and compliance? 
  33. How do we plan to continuously refine and optimize our AI models as we gather and incorporate additional data, ensuring ongoing relevance and performance improvement? 
  34. What are the possibilities for AI to narrow asymmetries between banks and their customers? 
  35. How can we deploy AI strategically to mitigate the information gap between financial institutions and customers, thereby fostering greater transparency and trust within the banking ecosystem? 
  36. In leveraging AI to bridge the divide between customers and banks, what methodologies can seamlessly integrate AI technologies, effectively closing service disparities and enhancing overall customer satisfaction? 
  37. What operational domains can be optimized through AI implementation?  
  38. Is AI a viable replacement for traditional middle-office functions within banking institutions? 
  39. How do fintechs position themselves to play nicely with bank AI systems already in place? How do fintechs complement these AI bank systems? 
  40. How do fintechs specializing in data ensure that data doesn’t dilute the efficiency of AI systems within the bank?  
  41. What measures can ensure that data integration with bank AI systems not only enhances functionality but also safeguards against compromises in system efficiency or data integrity? 
  42. From a banking perspective, what strategic imperatives underpin adoption of AI technologies? 
  43. How do banks ensure that AI implementation is driven by operational needs rather than industry trends or competitive pressures? 
  44. What role can AI play in modernizing or automating back-office processes and streamlining operations for FIs? 
  45. How has AI created access to new data in the FI? 
  46. How is AI enabling transformation opportunities across the bank? 
  47. What role will unstructured data play in the next significant wave of transformation in banking? 
  48. Many large-scale financial institutions have made significant investments in AI from model builders and cloud providers. How have FIs successfully integrated AI into core business processes?  
  49. What are the barriers to driving business value from AI? 
  50. How are organizations leveraging AI to extract business value from unstructured data? 
  51. How can AI enhance automation to drive straight-through processing in core processes? 
  52. How can AI help bankers work better, faster and smarter? 
  53. How can AI be leveraged responsibly across an institution?  
  54. How can banks optimize the benefits of AI without risking customer trust? 
  55. What measures should be implemented to continuously monitor and improve the accuracy and reliability of AI? Even with 99.99%, how can banks ensure they are not risking that .01%? 
  56. What departments or functions adopt AI first? 
  57. What are the key elements of a comprehensive AI strategy? 

Leaders from the following institutions contributed to this list: 

  • Amdocs  
  • Apiture 
  • Arteria AI  
  • BankTech Ventures  
  • Cotribute  
  • Glia  
  • Hummingbird  
  • Quantalytix  
  • Stratyfy 
  • TD Bank