- “AI runs on data, and banks and other multiline financial institutions (FIs) command vast, high-quality, customer-centric gold mines of data. In particular, the granular transactional data of a bank’s customer base can provide precise, wide-ranging insights into behaviors, preferences, needs, and risks in ways that few other industries’ data sets can. (See Exhibit 1.) For instance, in retail banking, leveraging AI to forecast and tailor future product offerings on the basis of customer needs and behaviors is rapidly becoming table stakes in many banking markets.
- Although numerous constellations of AI use are possible, many big opportunities that lie within end-to-end workflows—in particular, opportunities that marry predictive AI and GenAI in complementary ways—follow basic patterns. One such pattern consists of three steps: (1) process information; (2) evaluate/decide; (3) take creative action. In practice, this might be the workflow for replying to a customer inquiry, processing a supplier’s invoice, making a decision on a credit card application, monitoring an account for signs of money laundering, or writing a section of an investment prospectus. (See Exhibit 6.)
- A recent BCG study in collaboration with MIT Sloan Management Review found that organizations that successfully integrate responsible AI practices into the full AI product life cycle realize more meaningful benefits. In fact, the likelihood of making full use of the benefits of predictive AI nearly triples, jumping from 14% to 41%, when companies become leaders in responsible AI.”
https://www.bcg.com/publications/2023/a-genai-roadmap-for-fis