Whitepaper · Banking & AML
AI fraud detection for financial institutions
Whitepaper
PDF · 3 pages · on request
- Reading time
- 6 minute read
- Written for
- For heads of compliance and risk, fraud and aml analysts, bank ctos and data teams
- Related service
- AI Engineering & Predictive Analytics
Key takeaways
- 01
From rules to risk scores
Gradient-boosted models such as CatBoost and XGBoost find non-linear patterns in tabular financial data that static rules miss.
- 02
Reading the narrative
NLP on transaction narratives and watchlists adds signal that numbers alone cannot capture.
- 03
Millisecond integration
FastAPI endpoints connected to IBM DB2 and MySQL score each transaction without slowing the payment flow.
- 04
Explainable by design
SHAP attributions give every flag a human-readable reason that can be defended to auditors.
- 05
Models that stay sharp
Drift monitoring and scheduled retraining keep accuracy from decaying as behaviour changes.
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