Detection of Risk Management In Financial Sector Using Random Forest Algorithm

Abstract

Fraud detection is a crucial aspect of risk management in the financial sector, where early identification of fraudulent activities—such as credit card fraud, money laundering, and identity theft—can prevent significant financial losses. Traditional detection methods often struggle with evolving fraud patterns, necessitating more advanced approaches. This chapter explores the application of Random Forest (RF) and K-Nearest Neighbours (KNN) in fraud detection. The Random Forest model is trained on diverse transaction datasets, analyzing key features such as transaction frequency, amount, location, and device usage. By employing an ensemble learning approach, RF builds multiple decision trees using random subsets of data and features, improving accuracy and robustness in detecting subtle fraud patterns. In contrast, KNN is an instance-based learning algorithm that classifies new transactions based on the majority class of their nearest neighbours.
By leveraging both Random Forest and KNN, this project aims to enhance fraud risk management, enabling financial institutions to detect fraud more efficiently, minimize losses, and strengthen customer trust. The proposed framework provides a scalable and effective fraud detection system that integrates seamlessly into existing risk management infrastructures. Future work may explore real-time data streaming and hybrid models to further enhance adaptability and performance.