Exploring the Use of Big Data in Financial Risk Management and Fraud Detection

The banking and financial sector produces large volumes of data every thirty seconds, which makes it a datarich business. This data is classified as big data, defined as vast amounts of complicated, varied, and ever-expanding data. Financial organizations may use this data to get a better understanding of their clientele, product efficacy, and market trends. Nearly half of individuals have begun using electronic gadgets due to the advent of digital banking, which makes it simpler for consumers to do novel tasks, interact with companies, and evaluate goods and services. Banks may use this data to generate client profiles, which they can then examine to forecast customer behaviour and tailor services. This paper explores the use of big data within finance today, as well as the opportunities and problems that still need to be solved. The Random Forest model is considered to evaluate the processes of identifying fraudulent activities and managing risks by using Big Data. The predictive model potentially maintains the accuracy of the analysis.