FINANCIAL FRAUD DETECTION USING DATA ANALYTICS

Abstract

Financial fraud is a growing concern in the digital age, where the volume and complexity of financial transactions have increased dramatically. Traditional fraud detection systems, which rely on static rules and manual audits, often fail to identify sophisticated and evolving fraudulent activities. This research paper explores the application of data analytics as a proactive and intelligent approach to financial fraud detection.

The study investigates various analytical techniques, including supervised learning models such as logistic regression and decision trees, as well as unsupervised methods like clustering and anomaly detection. These tools help uncover hidden patterns and irregularities in large datasets that may indicate fraudulent behavior. Additionally, the paper examines the role of natural language processing (NLP) and network analysis in analyzing unstructured data and identifying collusive networks.

A structured framework for implementing data-driven fraud detection is proposed, covering data collection, preprocessing, model development, evaluation, and deployment. Real-world case studies from the banking and insurance sectors illustrate the effectiveness of these techniques in reducing fraud losses and improving detection accuracy.

The paper concludes that integrating data analytics into financial systems is essential for early fraud detection, regulatory compliance, and maintaining trust in financial institutions. Continuous model refinement and ethical data use are key to long-term success.

This research contributes to the growing body of knowledge on financial fraud prevention by demonstrating how data analytics can shift organizations from reactive to proactive fraud management. It provides actionable insights for financial institutions, policymakers, and researchers seeking to harness data-driven technologies to safeguard financial integrity and promote trust in the digital economy