The impact
Reduction in Account Takeover Incidents
0%
achieved within six months of full deployment
Fewer False Positive Alerts
0%
decrease in unnecessary customer friction and blocks
Faster Fraud Detection
0x
improvement in detection speed versus the previous rule-based system
Digital Channel Recovery
0%
growth in digital channel adoption in the quarter following deployment
Overview
Across Southeast Asia, rapidly digitising banks are facing a fraud landscape that has outpaced the controls built to manage it. For one mid-sized regional bank, that gap became a crisis. Over 18 months, a 210% surge in account takeover incidents — driven by credential-stuffing attacks, SIM-swap schemes, and sophisticated social engineering — exposed the hard limits of a rule-based detection system calibrated for an earlier threat era.
The system generated excessive false positives while simultaneously missing novel attack patterns that fell outside predefined thresholds. The cost was threefold: direct financial losses from fraudulent transactions, an overwhelmed fraud operations team manually reviewing thousands of alerts, and measurable damage to customer trust reflected in declining net promoter scores.
Static rules could not keep pace with adaptive adversaries. The bank needed a fundamentally different approach — one capable of learning from evolving attack signatures in near real time.
The objective
Replace a Rule-Based System With Machine Learning That Adapts as Fast as the Threat Does
The goal was to deploy a fraud detection architecture that could identify both known fraud patterns and emerging attack vectors it had never seen before — without increasing friction for legitimate customers or overwhelming operations teams with noise.