BE - September 2026

have always been constrained by how many scenarios a team could compute in the time available. That constraint has been largely lifted. Banks that use the new capacity to understand their own balance sheets more deeply, rather than to produce more reports, will be better prepared for the next shock than those that do not. One thread runs through all four. In each case, the value comes from the bank’s own data, applied to the bank’s own decisions. None of them can be bought off the shelf, which is exactly why it is defensible. THE UNGLAMOROUS OBSTACLES The obstacles are largely shared with the rest of the industry, but two have a regional edge. Data is the real bottleneck. Almost every bank that has attempted a seri- ous AI program has discovered that the hard part is not the model but the state of the underlying data. Cus- tomer records are stored across sev- eral systems that disagree. Contract terms live in scanned documents that nobody has structured. Historical data was never stored with analysis in mind. Fixing these takes years, produces nothing visible while un- derway, and is the precondition for everything else. It is also the part most often deferred in favor of a pilot that can be demonstrated. Talent cannot simply be bought. Banks compete for machine-learning specialists against technology firms that can offer equity, and a hired spe- cialist who does not understand credit or compliance produces mod- els the business will not trust. The in- stitutions making progress are training existing risk, operations and audit staff into hybrid roles alongside their specialist hires. That route starts slowly and lasts longer because the the BANKING EXECUTIVE 48 ISSUE 213 SEPTEMBER 2026