Explainable AI for Payment Fraud Detection

In the world of fraud detection, accuracy isn't everything. Understanding why a transaction is flagged as suspicious is equally important.

The Problem with Black Boxes

Traditional ML models often act as black boxes—they make predictions but don't explain their reasoning. This creates problems:

Regulatory compliance - Financial institutions must explain decisions

Trust - Users need to understand why they're flagged

Debugging - Engineers need to fix model errors

Our Approach

We combined classical ML algorithms with SHAP (SHapley Additive exPlanations) to create transparent predictions.

The Tech Stack

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Results

Our hybrid approach achieved 94% precision while providing human-readable explanations for every prediction.

import shap
import xgboost as xgb

Train model
model = xgb.XGBClassifier()
model.fit(X_train, y_train)

Generate explanations
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

Visualize
shap.summary_plot(shap_values, X_test)

Conclusion

Explainability isn't a luxury—it's a necessity for deploying ML in sensitive domains like fraud detection.