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Make your own version of Fraud Risk Analytics

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Original by 20524UCSC059 RAJESHWARI V

Describe your app

Module 1 – User Authentication Manages secure user registration and login. Module 2 – Transaction Management Collects and manages credit card transaction details. Module 3 – Data Preprocessing Cleans and prepares transaction data for analysis. Module 4 – Behavioral Analysis Analyzes user spending and transaction patterns. Module 5 – Fraud Detection Uses LSTM and XGBoost to identify fraudulent transactions. Module 6 – Risk Analysis Generates Low, Medium, or High risk levels. Module 7 – Explainable AI Uses SHAP to explain the reasons behind predictions. Module 8 – Dashboard & Reporting Displays transaction statistics, fraud results, and reports. Module 9 – Admin Management Allows administrators to monitor users and fraud activities. Users Table User_ID Name Email Password Role Transactions Table Transaction_ID User_ID Amount Time Location Merchant Predictions Table Prediction_ID Transaction_ID Result Confidence Risk_Level Reports Table Report_ID User_ID Report_Type Date Programming Python Frontend HTML, CSS, Bootstrap Backend Flask Database MySQL Data Processing Pandas, NumPy, Scikit-learn AI / ML SMOTE, LSTM, XGBoost Explainable AI SHAP Visualization Matplotlib / Plotly ALGORITHM WORKING SMOTE Balances the imbalanced fraud dataset. LSTM Learns sequential transaction and behavioral patterns. XGBoost Classifies transactions as fraud or genuine. SHAP Explains the important features behind the prediction. Main Screens Login Page – Secure user access. Dashboard – Displays fraud statistics. Transaction Page – Enter transaction details. Prediction Page – Shows fraud/genuine result. Risk Analysis – Displays risk level and confidence. Explanation Page – Shows SHAP-based reasons. History Page – Displays previous transactions. Admin Page – Manages users and fraud cases.

Free to use. No credit card required.