The Financial Services Playbook for Effective Data Maturity

The financial services industry — from the boardroom to the customer — finds itself in a transformative era. Banks, insurers, and financial services firms are hyper focused on expanding existing relationships and attracting new customers. They’re doing it by putting an operational spotlight on cultivating more immersive experiences while simultaneously curbing costs — with an eye on AI as the magic bullet to achieve it all. It’s being driven by the need to meet elevated customer expectations for personalized services and products. It’s clear customers will go to whatever organization provides them the best experience, and the competition is also racing to adopt AI as the barriers to entry are low.

But in the mad dash to align operational transformation with evolving customer expectations, many analytics and IT leaders are ignoring the basics: The potential of AI to solve these problems is intertwined with the quality and trustworthiness of the underlying data. There must be an understanding that the diligent, yet sometimes time-consuming work, to ensure data is accurate and complete is a necessary investment for the payoff of high-quality insights. The basic, simple work to create a data foundation at the outset is necessary for any future AI for financial services initiative.