International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
AI-BASED REAL TIME FRAUD DETECTION SYSTEM FOR FINTECH SECURITY Tharuni.A1, Shashidhar Nayak.B2, Suryanandan.G3, Aswin.G4 1234Department of Information Technology, TKR College of Engineering and Technology, Telangana, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rapid adoption of Unified Payments
necessitating intelligent, automated approaches for fraud detection. Machine learning (ML) techniques have emerged as effective tools for identifying anomalous patterns in transactional data. By leveraging algorithms such as Random Forest, Decision Tree, and Logistic Regression, it is possible to detect potentially fraudulent transactions in real-time. These models analyze multiple transaction attributes, including amount, timing, frequency, device type, geolocation, and user behavior patterns, to classify transactions accurately. This paper presents a UPI fraud detection system integrated into a Django web application, offering both administrators and end-users a practical solution to enhance transaction security. The system enables model training, real-time fraud prediction, and visualization of performance metrics, providing a comprehensive framework for safeguarding digital payments.
Interface (UPI) in India has transformed digital payments by enabling instant, convenient, and secure transactions. However, the increasing use of UPI has also led to a surge in fraudulent activities, including phishing, fake applications, unauthorized transactions, and social engineering attacks. Detecting such fraudulent transactions in real-time is essential to ensure user trust and maintain the integrity of digital payment systems. This paper proposes a machine learningbased UPI fraud detection system that employs supervised learning algorithms—Random Forest, Decision Tree, and Logistic Regression—to classify transactions as genuine or fraudulent. The system analyzes various transaction attributes such as amount, frequency, timing, device type, geolocation, and user account characteristics to identify anomalous patterns indicative of fraud. Performance is evaluated using standard metrics including accuracy and confusion matrices. Furthermore, the system is deployed via a Django web application, enabling administrators to train models and visualize performance through graphical representations, while end-users can input transaction details to receive real time fraud predictions. The proposed approach enhances financial security, strengthens user confidence in digital payment platforms, and demonstrates the practical effectiveness of machine learning in safeguarding digital transactions.
1.1 Need for Real-Time Fraud Detection in FinTech System The rapid growth of Financial Technology (FinTech) platforms—including digital payments, online banking, mobile wallets, and cryptocurrency exchanges—has significantly increased the volume and velocity of financial transactions. While these advancements improve user convenience and financial inclusion, they also expose systems to sophisticated fraudulent activities such as identity theft, account takeover, transaction laundering, and payment fraud.
Key Words: Artificial Intelligence, Real-Time Fraud Detection, FinTech Security, Machine Learning, Anomaly Detection, Cybersecurity.
Traditional fraud detection systems rely heavily on rulebased mechanisms and offline analysis, which are often ineffective against modern, evolving fraud patterns. These systems suffer from delayed detection, high false-positive rates, and poor adaptability to new attack strategies. As financial transactions occur in milliseconds, there is a critical need for real-time fraud detection systems capable of instantly analysing transaction behaviour and preventing fraudulent activities before financial loss occurs. Hence, intelligent and automated security mechanisms are essential to safeguard FinTech ecosystems.
1. INTRODUCTION The advent of digital payment systems has significantly transformed the financial landscape worldwide, with India witnessing a major shift through the Unified Payments Interface (UPI). UPI facilitates instant, secure, and convenient money transfers between bank accounts using mobile devices, eliminating the need for physical cash or card-based transactions. Its rapid adoption has contributed to financial inclusion and enhanced transaction efficiency. However, the widespread use of UPI has also exposed users and financial institutions to various fraudulent activities, including phishing attacks, fake UPI applications, unauthorized transactions, and social engineering exploits. Traditional manual monitoring methods are inadequate to handle the scale and complexity of these threats,
© 2026, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 370