AI, data, and trust: A leadership view on the future of financial services

Managing this data effectively is a core challenge. Financial services companies must navigate fragmented tools and legacy systems, all while trying to innovate with AI. Minetola emphasized that data access and quality are fundamental: “The value of the GenAI promise will only be as good as the data that you can access and your ability to effectively get the right answer.”

For financial institutions that want to use AI to modernize their operations, there are challenges. "If you don't have a work in progress already to create an end-to-end data repository, or you're [not] already looking to clean up your data, that's got to be the center point of your strategy," said Minetola. He emphasized the need to simplify and consolidate tools, warning that "gluing won't scale" when it comes to managing unstructured data.

Andress noted that the pressure to innovate must be balanced with security and compliance. "Financial institutions today are facing a perfect storm of challenges regarding security. The sophistication of cyber threats continues to grow, with targeted ransomware, phishing, and insider attacks becoming more frequent. At the same time, regulatory requirements are increasing." Furthermore, Kulkarni noted, generative AI can significantly improve how financial institutions serve customers: "GenAI, for the first time, is providing this opportunity for all of this analysis to be done in an automated way — and through that process, massively reduce the expenses, improve the customer experience; transform the business." The key is finding ways to harness AI while maintaining control and trust. As Minetola puts it, "Trust and confidence by the customer and by the organization is paramount."

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