Driving value with data and AI: A guide for financial services institutions
In financial services, data is arguably as important a resource as the monetary assets these organizations oversee. Everyone from banks and investment firms to mortgage companies and payment processing organizations rely on data for sustainable growt
Understanding your “why” first
Defining value
Sample financial services outcomes and the initiatives that support them
In financial services, data is arguably as important a resource as the monetary assets these organizations oversee. Everyone from banks and investment firms to mortgage companies and payment processing organizations rely on data for sustainable growth, risk management, decision making and product or offering development. However, as digital transformation in the industry continues to expand and AI (especially GenAI) increases in demand and regulations shift, the value of their data grows at an exponential rate.
Laws and regulations, like the BCBS 239, Dodd-Frank Act, SOX, FATCA, the Bank Secrecy Act and EU Anti Money Laundering Directives help not only protect sensitive customer data, but also help ensure the safety of transactions while detailing the reporting requirements imposed upon these institutions. And to make things even more challenging, data and AI specific laws like GDPR, CCPA and the EU AI Act all play an important role in data privacy and protection, providing consumers an extended layer of control.
Financial institutions have been (trying) to keep up with data demands for years, however the explosion of GenAI tools and services has put even more pressure on data teams to provide even higher-quality data. Use cases from credit risk scoring and chatbots to agentic AI are all gaining traction, helping to create an enhanced customer experience and automating repetitive tasks for employees. Even traditional analytical models are having a renaissance with the need for faster and better outcomes across business units. With this demand however, there is a widening divide between what financial services institutions aspire to achieve with data and AI and what they are capable of. This discrepancy can lead to risky initiatives lacking a unified governance foundation.
While these problems seem disconnected, they boil down to one thing: governance fragmentation. Control, visibility and even meaning are tied to specific systems, sources and even compute platforms. And fragmentation extends to your people, as technical solutions aren’t accessible to the stakeholders who need to create policies, steward and use the data.
The organizations that overcome these challenges to build a solid foundation for data governance will accelerate and strengthen every data and AI use case — without the risk.
