Article

Fraud Detection using Risk Based Binary Classification

Gurpreet KaurState University , Gurdaspur

International Journal for Research in Applied Science and Engineering Technologyjournal2026

ABI

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.

Topics

Imbalanced Data Classification Techniques Financial Distress and Bankruptcy Prediction Advanced Malware Detection Techniques

Identifiers

DOI: 10.22214/ijraset.2026.76970

Citations and references

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