Hybrid Ensemble Learning Framework for Real Time Financial Fraud Detection Using Behavioral Transaction Analysis
TL;DR
The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction.
Abstract
The increasing use of online financial services, including online banking, mobile payment platforms, UPI transactions, and electronic fund transfers, has significantly increased the risk of financial fraud in today's digital economy. Conventional rule-based fraud detection approaches are often ineffective in handling continuously changing fraud techniques, leading to considerable losses for both customers and financial organizations. To resolve these limitations, this work presents a Hybrid Financial_Fraud Detection System that combines multiple machine_learning techniques to achieve higher_detection accuracy and enable real-time transaction analysis. The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction. The model evaluates several transaction-related parameters, including transaction amount, account balance, transaction frequency, average transaction value, account age, previous fraud history, transaction time, and indicators of foreign transactions. Customer details are automatically obtained from a customer database, allowing the system to perform transaction-specific and personalized risk assessment. A banking-oriented application_interface has been developed to enable users to carry out transactions through different payment channels such as UPI, bank_transfer, debit_card, credit_card, NEFT, RTGS, and IMPS. Each transaction is processed and analyzed instantly, assigned a fraud risk score, and categorized as either genuine or fraudulent. In addition, the system records transaction history, updates account balances, and provides a monitoring dashboard to support continuous fraud analysis. By integrating ensemble learning_techniques with behavioral transaction analysis and real-time monitoring, the proposed system improves the overall effectiveness of fraud detection. The framework is designed to strengthen transaction security, minimize financial_losses, and offer a scalable and intelligent solution for fraud prevention in modern digital financial environments.