Enhanced Online Payment Fraud Detection

TITLE	Enhanced Online Payment Fraud Detection
ABSTRACT	The quick development using making payments electronically has made money more accessible and convenient, but it has also raised the possibility of online payment fraud. Traditional rule-based security systems are still vulnerable to fraudulent activities including identity theft, transaction manipulation, and unauthorised transactions because they frequently fail to identify evolving fraud patterns in real time. This study suggests an Enhanced a machine learning -based mechanism for identifying fraudulent online payments successfully and precisely detects suspicious transactions in order to address this problem and identify fraudulent conduct. The system examines transaction-related elements such as transaction amount, payment type, frequency, user behaviour, and device-related information. To detect transactions as authentic or fraudulent, ML models are employed by the system. that have been trained, allowing for early intervention and loss avoidance. Real-time alerts and visual insights are provided by an intuitive interface to help consumers and financial institutions make wise choices. Through proactive, data-driven security measures, this intelligent fraud detection technology increases accuracy, lowers false positives, in addition to increases confidence in systems that online payments.
AUTHOR	Rakshitha C P, Maheshwari M Desai PG Student, Dept. of MCA, City Engineering College, Bengaluru, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, India
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1206027
PDF	27_Enhanced Online Payment Fraud Detection.pdf
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