Implementation of Big Data Techniques in Banking: Evidence from Real-time Fraud Detection and Customer Segmentation

In the constantly changing financial field, the combination of big data technology and the financial industry is very important to enhance the development of the financial industry. This research will investigate the application of big data in two key banking areas: real-time fraud detection and customer segmentation. In the case of JPMorgan Chase, this paper discusses how the bank used machine learning algorithms to monitor and analyze transaction data in real time, which greatly reduced the costs associated with fraud. At the same time, this study also studied Wells Fargo's utilization of big data tools in customer field, resulting in improved customer retention and cross-selling potential. The results of this study highlight the importance of big data in the banking industry, particularly in terms of operational efficiency, security and customer engagement. However, issues (e.g., privacy concerns and the need for expertise) remain important barriers. The study goes on to discuss the future possibilities of big data in banking, highlighting the potential for upcoming tools and schemes to further transform banking.