Advanced Data Analytics Techniques in Fraud Detection: Anomaly Detection and Predictive Analytics

Nrusingh Prasad Dash

Abstract: Fraud detection has become an increasingly important issue confronting organization. Scott and Forster outline frauds committed in financial, economic, insurance, banking, and telecommunication sectors. These types of fraud lead to legal repercussions, stunted organizational growth, remediation costs, and loss of credibility for organizations. The fraud detection lifecycle consists of prospecting, prioritization, investigation, and analysis. The detection of fraud attempts starts after prospecting. The goal is to reduce the risk of loss, and the computation of fraud risk scoring or ranking helps prioritize different fraud attempts. The term "anomaly" is defined by Iglewicz and Hoaglin (1993) as a "data point or observations that do not conform to an expected pattern". Anomaly and fraud detection can identify abnormal user activity, prevent high-stake losses from lax fraud detection systems and significantly reduce loss scopes. Despite its significance, fraud detection systems are still underdeveloped in many sectors, due to large data volume, speedy time stamps, information constraints, and elaborate analyzing techniques.

Keywords: Fraud Detection, Anomaly Detection, Predictive Analytics, Machine Learning, Financial Transactions, Risk Scoring, Class Imbalance, Temporal Data, Data Governance, Privacy- Preserving Analytics, Model Robustness, Regulatory Compliance

How to Cite?: Nrusingh Prasad Dash, "Advanced Data Analytics Techniques in Fraud Detection: Anomaly Detection and Predictive Analytics", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 47-57, https://www.ijsr.net/getabstract.php?paperid=SR251231110855, DOI: https://dx.doi.org/10.21275/SR251231110855