Improving Fraud Detection and Risk Assessment in Financial Service using Predictive Analytics and Data Mining

Abstract

The financial services sector has undergone a transformation due to predictive analytics and data mining, which have improved risk assessment and strengthened fraud detection capabilities for enterprises. This article outlines their major significance and gives an overview of how they are used in the financial sector. The study describes how data mining and predictive analytics methods make use of vast amounts of financial data to find trends, connections, and insights. It focuses on their application in fraud detection and risk assessment in particular. In risk assessment, credit risk, market volatility, and liquidity risk are predicted using historical data, statistical modeling, and machine learning algorithms. These methods support regulatory compliance, portfolio management, and well-informed decision-making. When it comes to detecting and stopping fraudulent activity, such as identity theft, payment fraud, and insider trading, predictive analytics and data mining approaches are essential. Finding possible fraudulent transactions and activities mostly depends on anomaly detection, pattern identification, and behavior analysis. The essay also discusses the difficulties and moral issues such as data privacy, justice, and interpretability that come with using data mining and predictive analytics in the financial services industry.