Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers and employees. The sooner a bank detects fraud, the faster it can restrict account activity to minimize loses.
Big Data in Banking: Use Cases in 2020 and Beyond
Case study: ING Direct taps big data to understand customers | ZDNet
Big Data in Banking Sector
ING Direct wanted to get into the heads of customers, so the bank started a data-collection initiative to gain a deeper understanding of how it was interacting with customers. Now, years later, ING Direct faces the problem of having too much data, and is trying to make sense of all of the information in a useful and cost-effective way. This has prompted ING Direct to dabble in big-data solutions to expedite the process of using all of the collected data to help make business decisions.