This is like having a tireless digital auditor that watches every claim or transaction in real time, compares it against millions of past patterns, and quietly flags the ones that look suspicious so humans can step in before money is lost.
Traditional rule-based fraud checks miss new fraud patterns and generate many false alarms, while manual reviews are slow and expensive. AI-driven fraud detection continuously learns from data to spot abnormal behaviours and emerging fraud schemes earlier, reducing loss ratios and investigation costs.
Access to large, labelled fraud and non‑fraud datasets, integration into core claims/transaction workflows, and continuous model retraining pipelines that adapt to new fraud patterns.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring latency and maintaining model performance as data distributions and fraud patterns drift over time.
Early Majority
Likely focuses on combining supervised fraud models with anomaly detection over complex relational data and rules, tailored for insurance and high-complexity transactions, rather than generic payment-card fraud only.