Explainable and Privacy-Preserving Fraud Detection in Mobile Banking

An explainable and privacy-preserving fraud detection system for mobile banking.

An explainable and privacy-preserving fraud detection system for mobile banking. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are explanations of each result (feature importance or visual evidence), security and privacy controls such as encryption and access control, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses SHAP, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.