What I Learned About AI Trust from Reconciling over 100B Transactions

A financial services operator shares insights on AI trust gained from processing over 100 billion transactions, emphasizing that trust issues often stem from governance and data definition problems rather than model failures. Using the example of how different teams interpret "monthly active users" differently based on system-generated SMS charges, the author demonstrates how unclear data definitions cascade through AI systems, causing downstream models to make flawed decisions. The key lesson is that AI trustworthiness requires establishing clear governance frameworks and agreed-upon data definitions from the start, not just building better algorithms.