This is like having a smart security guard watching all your financial transactions in real time, learning what “normal” looks like for each customer and immediately flagging or blocking anything that looks suspicious or out of character.
Traditional fraud rules miss new fraud patterns and generate many false alarms, causing financial losses, chargebacks, and operational overhead. AI-based fraud detection continuously learns from data, spots subtle and evolving fraud patterns, and reduces both undetected fraud and unnecessary manual reviews.
Proprietary transaction and behavior data combined with continuously improving detection models and integration into core payment and risk workflows can create a strong data and workflow moat.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring latency and the need to maintain high model performance as transaction volume and fraudster behavior scale.
Early Majority
Positioned as AI-first, with adaptive models that learn from new fraud patterns faster than static rules, and potentially offering more granular, behavior-based risk scoring rather than broad rule-based declines.