GST Anomaly Detection System

Welcome to the Machine Learning Project Presentation series! In this video, we present “GST Anomaly Detection System” — a FinTech-focused project built using a synthetic dataset simulating real GST transactions to combat tax fraud. 💻 Project Overview In today’s economy, GST fraud and fake Input Tax Credit (ITC) claims are a significant and increasing problem. Manual verification of invoices is time-consuming, prone to errors , and existing systems often fail to detect subtle or complex fraudulent patterns. To solve this, we’ve built a Machine Learning-based Anomaly Detection System using the Random Forest algorithm. It analyzes key invoice and business features—like ITC claimed, filing delays, and turnover —to accurately detect and classify fraudulent transactions. 🧩 Technology Stack & Workflow 🔹 Dataset: Synthetic dataset simulating real GST transactions (5000 Invoices) 🔹 Features: Invoice Value, IGST, CGST, SGST , Input Tax Credit (ITC) , Turnover , Invoice Count , BusinessAge , Filing Delay 🔹 Algorithm: Random Forest Classifier 🔹 Process Flow: Data Collection (Using synthetic GST data) Feature Engineering (Using features like ITC, Filing Delay, Turnover) Model Training (Using Random Forest Classifier) Model Evaluation (Using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix) Fraud Classification (Classifying transactions as normal [0] or fraud [1]) ⚙️ Key Features ✅ Automated detection of fraudulent GST transactions ✅ Trained on key business features like ITC, Filing Delay, and Turnover ✅ High performance: Achieved 93.5% accuracy and 98% recall for fraud class ✅ Uses Random Forest, which handles complex relationships and provides feature importance ✅ Capable of real-time alerts for suspicious transactions 🌍 Applications 💰 Automated GST fraud detection for tax authorities 📊 Real-time invoice verification systems ⚠️ Real-time alerts for suspicious transactions 🕵️ Assisting auditors with explainable AI (as a future scope) 🏁 Conclusion This project successfully built a machine learning model to detect fraudulent GST transactions. The Random Forest model demonstrated high accuracy and recall , effectively identifying key fraud indicators like Filing Delay and Input Tax Credit. This system provides a strong foundation for an automated, real-time fraud detection solution. 🔖 Hashtags #MachineLearning #AnomalyDetection #FraudDetection #GST #FinTech #RandomForest #GSTFraud #DataScience #AIProjects #MLProjects #Python #TaxFraud #InputTaxCredit #ITC #ScikitLearn #FinancialFraud #AI #RealTimeDetection #DataAnalytics #FeatureEngineering