When Trust in the Old Model Cost Customers: A Fintech's Slow Update
A real-world example of Conservatism in action
Context
A mid-stage fintech company processed millions of transactions monthly and relied on a legacy rule-based fraud system built two years earlier. Product analytics and customer support were flagging a rising number of false positives—legitimate transactions blocked or sent for manual review.
Situation
Data science built a new machine-learning model that cut false positives by 40% in offline validation and reduced manual-review volume substantially. Engineering prepared a phased rollout and A/B test, but the fraud operations and compliance leads hesitated to deploy, citing past model failures and the need to “be cautious with user-facing changes.”
The bias in action
Decision-makers anchored on the prior system’s perceived stability and gave insufficient weight to the new model’s validation results. They demanded excessive extra evidence and withdrew from the planned A/B test after a few anomalous signals, treating limited noisy feedback as confirmation of their prior skepticism. As a result, the rollout was delayed and the organization continued to prioritize explanations that fit the old model’s trustworthiness rather than revising beliefs in light of strong new data.
Outcome
Because the new model was never fully deployed, the company continued to block or flag many legitimate transactions. Customer complaints and abandonment increased, operational costs remained high due to manual reviews, and product growth slowed while competitors offering smoother experiences captured share.