💸 UPI Fraud Detection with Machine Learning
ABSTRACT
With the increasing adoption of digital payment platforms in India, especially **Unified Payments Interface (UPI)**, there has also been a dramatic rise in online fraud cases. Traditional rule-based systems are no longer sufficient to tackle sophisticated fraud patterns. This project introduces an intelligent **UPI Fraud Detection System** powered by **Machine Learning** to identify suspicious transactions in real time.
By analyzing features like transaction amount, time, frequency, location, UPI ID behavior, and user demographics, the system can learn normal patterns and detect anomalies. The model is trained using historical transaction datasets and uses techniques like anomaly detection and classification algorithms to flag fraudulent behavior.
EXISTING SYSTEM
The current UPI transaction monitoring tools are based on pre-defined static rules such as:
- Transaction amount exceeding ₹50,000
- Unusual time of transfer
- Suspicious device/location combinations
However, these systems fail to detect evolving fraud strategies, often resulting in false positives or missed cases. They also lack adaptability and real-time learning capability.
PROPOSED SYSTEM
The proposed system uses **machine learning algorithms** to intelligently detect fraudulent UPI transactions by learning from historical data. Unlike static systems, it adapts over time as it is exposed to new data.
- 🔍 Detects behavioral anomalies using user patterns
- 🧠 Uses supervised learning to classify transactions as fraud/non-fraud
- 📡 Operates in real-time with low latency
- 🔐 Supports alerting and automatic transaction blocking
TECHNIQUES USED
- 📊 Data Preprocessing: Cleaning, normalization, handling missing values
- 🧠 Modeling: Random Forest, Logistic Regression, and CNNs
- 📈 Anomaly Detection: Local Outlier Factor, Isolation Forest
- ⚙️ Real-Time Classification: Model predicts on new transactions as they occur
MODULES
- 🗃️ Dataset Preprocessing & Feature Engineering
- 🧠 Model Training using labeled transaction data
- 📡 Live Transaction Monitoring Module
- 🚨 Alert & Fraud Flagging System
- 📊 Admin Dashboard for Fraud Analytics
ADVANTAGES
- ✅ Real-time fraud detection & prevention
- ✅ High accuracy with machine learning adaptation
- ✅ Detects evolving fraud tactics
- ✅ Reduces false positives compared to rule-based systems
- ✅ Easily scalable to large banking systems
SOFTWARE & TECH STACK
- Programming Language: Python
- ML Framework: Scikit-learn, TensorFlow
- Web Framework: Flask
- Frontend: HTML, Bootstrap
- Database: SQLite or MySQL
HARDWARE REQUIREMENTS
- Processor: i3 or above
- RAM: 4 GB minimum
- Disk: 100 GB
📽️ DEMO VIDEO
DELIVERABLES
- 📁 Source Code + Pre-trained Model
- 📄 Project Report / Documentation
- 🧭 Execution Manual & ReadMe
- 📸 Screenshots + Result Snapshots
- 🎥 Video Tutorials
- 📊 Complete Dataset & Complete UI
CONCLUSION
UPI Fraud Detection using Machine Learning offers a robust, intelligent solution to combat the growing threat of digital payment fraud. With AI-based transaction monitoring, the system minimizes risks, improves security, and builds user trust in cashless economies.
📩 SUPPORT
Need help with setup or customization? Reach out to our team: xpertieee@gmail.com
