Walsh suggests that banks should start by identifying the best use cases for AI that offer quantifiable business value, such as revenue enablement, cost reduction, or risk mitigation. He also highlights the role of AI in enhancing security by predicting and preventing cyber threats. Furthermore, he advises banks to identify two or three high-impact use cases to 'fail fast', leverage their learnings, and execute effectively. He concludes by stating that AI holds enormous potential for transforming middle and back-office functions, and that not implementing AI is a decision not to act, which could leave banks sidelined and marginalized.
Key takeaways:
- AI deployment at scale can improve top and bottom line growth, and is full of opportunities for banks to cater to different customer needs and behaviours.
- AI can play a key role in areas such as fraud detection, credit analysis, risk and finance management, workforce planning, and security.
- Starting with AI is easier than it seems, with the recommendation to identify two or three high-impact use cases to 'fail fast' and leverage learnings for effective execution.
- AI holds enormous potential for transforming middle and back-office functions, and not acting on this potential is a decision that could leave banks sidelined and marginalized.