Fraud Detection using Risk Based Binary Classification
Abstract
A Google Pay fraud detection system is proposed in this research, implemented in C++, which detects fraudulent transactions through a risk-based binary classification framework. The system analyzes key factors such as transaction amount, transaction frequency, device trust, and location mismatch to determine transaction legitimacy A binary output is generated by the system, with 0 denoting legitimate transactions and 1 denoting fraudulent transactions. The study highlights how machine learning–inspired approaches can be applied to real-time payment systems to improve security and prevent financial losses.
Fields of Study & Subjects
Imbalanced Data Classification Techniques
Privacy-Preserving Technologies in Data
User Authentication and Security Systems
Publication details
- Journal / source
- International Journal for Research in Applied Science and Engineering Technology
- Volume
- 14
- Issue
- 1
- Pages
- 1864–1866
- Publication date
- 31 January 2026