Explainable AI Agent for E-Commerce Return Risk and Fraud Detection

An explainable AI agent for e-commerce return risk and fraud detection.

An explainable AI agent for e-commerce return risk and fraud detection. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend 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), cooperating agents with defined roles and tools, an order and payment flow, data preparation and exploration, and model training and comparison of algorithms. The reference build uses LLM APIs, SHAP, Razorpay, Python, and scikit-learn, with a clear project structure, setup instructions and sample data so that it can be run and extended.