Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

Abstract

This study examines the impact of machine learning algorithms on risk management and fraud detection in financial institutions. Machine learning has the potential to be integrated into various decision-making systems. In recent years, the ability of machine learning algorithms to analyze large datasets and provide precise predictions has attracted significant attention across multiple sectors. Accurate risk assessment and fraud prevention are crucial for financial institutions to avoid potential financial losses and damage to their reputation. Machine learning algorithms have enabled financial institutions to adopt more efficient and effective strategies to mitigate these risks. Financial institutions increasingly utilize machine learning algorithms to improve risk management and fraud detection. These algorithms employ advanced statistical techniques to analyze massive datasets and identify patterns and anomalies that may indicate potential risks or fraudulent activities. The most widely used machine learning algorithms in risk management and fraud detection are supervised learning algorithms like logistic regression and decision trees and unsupervised learning algorithms like clustering and anomaly detection. These algorithms can process and analyze vast amounts of data in real time, allowing financial institutions to detect and respond more effectively to potential risks and fraudulent activities.