CASE STUDY / 02
Real-time fraud intelligence
Use language signals to help identify fraudulent banking transactions.
Responsibility
The résumé describes developing a banking-client fraud detection system using BERT and XGBoost during the Cognizant role.
Architecture at a glance
- Transaction context
- BERT signals
- XGBoost scoring
- Risk review
Reported outcome
$2M+ reported annual savings
Annual savings is a business outcome reported in the PDF alongside a 40% reduction in fraudulent transactions; it is not a model accuracy score.
The loss baseline, annualization method, review costs and attribution method are not published. This is a reported system outcome, not a claim of independently verified personal savings.
Read the source résumé (PDF)Technical deep dive
Design decision
Pair language representations with a decision model so unstructured context can inform risk scoring.
Alternatives and tradeoffs
Rules offer straightforward explanations but need maintenance as patterns change. A learned model needs threshold calibration and monitoring for false positives.
Engineering takeaway
Assess false-positive cost alongside detected fraud. Review capacity and changing transaction patterns matter as much as offline model scores.