A leading retail bank had deployed a machine learning model to detect transactional
fraud. At launch, performance was strong. Over time, fraud patterns shifted, customer
behaviour changed, and model accuracy began to degrade — increasing false positives
while missing new fraud signals.

With AI Smartsource, model performance was continuously evaluated against live
transaction data. Drift was identified early across key features and retraining cycles
were embedded into the run model — turning optimization into an ongoing process, not
a periodic fix.
The outcome:
●Fraud detection accuracy continuously improving as patterns evolve
●False positives reduced — fewer legitimate transactions declined
●Retraining shifted from reactive fixes to continuous optimization
●Leadership gained real-time model performance visibility