FRAUD APP DETECTION USING MACHINE LEARNING

Abstract

Due to the rise in online transactions and fraud, strong solutions are needed to protect financial
transactions and sensitive data. A new supervised machine learning method classifies dangerous and benign
network fraud applications. Supervised learning and feature selection were utilized to develop the optimum
detection success rate model. This research demonstrated that Random Forest-based machine learning with
wrapper feature selection classifies network fraud applications better than SVM. The dataset is used to identify
network fraud applications using SVM and RANDOM FOREST supervised machine learning to assess
performance. Comparative analysis demonstrates that our model outperforms other methods in Fraud
Application detection.
Rule-based fraud detection systems fail to adapt to changing fraud methods and trends. High false positive rates
and frequent manual upgrades characterize these systems. They cannot identify new fraud trends, leaving
security weaknesses.
We propose a Machine Learning-based Fraud App to address these constraints. The software can understand
complicated fraud patterns by studying past data and using advanced ML algorithms, enhancing accuracy and
flexibility. This allows real-time fraud detection with fewer false positives, improving user experience by
reducing security alarms. Adaptive learning, continual improvement, and fraud pattern detection are benefits of
the suggested system. The app's powerful ML models boost fraud detection accuracy and keep up with new
threats, improving security, fraud losses, and online transaction confidence for financial institutions and
customers.