Fraud Transaction Detection System

Enterprise Risk Intelligence Platform Inspiration Digital payments are growing rapidly, but so are fraudulent transactions. We were inspired by a simple yet critical problem: most fraud detection systems either act too late or operate as black boxes that cannot explain their decisions.

In real-world banking, it is not enough to say “This transaction is fraud.” Institutions must explain why, quantify risk, and ensure models remain reliable over time.

We wanted to build not just a fraud detection model, but a transparent, intelligent, and self-monitoring risk platform.

What We Learned Throughout this project, we learned:

Fraud datasets are highly imbalanced (fraud < 1%), making accuracy misleading.

Evaluation metrics like:

Precision Recall

and AUC‑PR are more meaningful than plain accuracy.

Threshold tuning directly impacts business trade-offs between false positives and missed fraud.

Explainability (SHAP) is essential for regulatory trust.

Monitoring data drift is critical to prevent silent model degradation.

How We Built It Data Preprocessing We used the ULB Credit Card Fraud dataset. We:

Scaled financial features

Handled class imbalance using SMOTE

Applied stratified train-test splitting

Model Development We trained an optimized XGBoost classifier achieving:

ROC‑AUC: 0.97

Precision: ~0.90

PR‑AUC: 0.81

Instead of binary output, we generated a Fraud Risk Score:

Risk Score=P( fraud ) × 100 This allows risk-based decision making.

3️⃣ Explainable AI Layer Using SHAP values, we identified the top drivers behind each flagged transaction, making predictions transparent and auditable.

4️⃣ AI-Powered Risk Narration We integrated a generative AI layer to convert technical model outputs into clear business explanations, enabling non-technical stakeholders to understand fraud decisions.

5️⃣ Drift Monitoring We designed the system to compare live transaction distributions with training data to detect behavioral shifts and trigger retraining alerts.

⚠ Challenges We Faced Handling extreme class imbalance without overfitting

Avoiding data leakage during SMOTE application

Balancing precision vs recall for real-world usability

Making anonymized PCA features interpretable

Designing explainability that is understandable to non-ML audiences

🌍 Impact Our solution transforms fraud detection from a reactive rule-based system into a proactive, explainable, and continuously monitored risk intelligence platform.

It is not just a model — it is a deployable, enterprise-ready fraud risk system built for transparency, trust, and long-term reliability.