Big Data Analytics for Fraud Detection and Risk Mitigation in Financial Services

Abstract

This study examines how big data analytics is transforming fraud detection and risk mitigation in financial services. The rapid expansion of digital finance has amplified cybercrime, money laundering, and complex cross channel fraud, exposing the limitations of traditional rule based and manual controls. Using a systematic literature review approach, this paper synthesizes evidence on the deployment of big data platforms, machine learning, and deep learning across banking, insurance, and capital markets. The findings show that analytics driven systems, particularly those combining supervised and unsupervised techniques, enhance the detection of anomalous behavior, capture rare and non linear fraud patterns, and support continuous, real time surveillance of transaction populations. Beyond transaction level monitoring, big data analytics is increasingly embedded in early warning systems, credit scoring, stress testing, and portfolio monitoring, thereby strengthening credit, operational, and reputational risk management. However, persistent challenges related to data quality, legacy infrastructures, model transparency, regulatory and ethical concerns, and shortages of analytics skills constrain the full realization of these benefits. Overall, the review underscores that big data analytics delivers the greatest value when integrated into institution wide risk governance frameworks that align technology, data governance, regulation, and human expertise.