Detecting fraud at the account and transaction levels is critical. The rise of digital banking has provided new opportunities for fraudsters to test novel methods of stealing from institutions and clients. In addition, the organization required a strong basis for data governance and modeling so that data could be widely used for analytical and data science use cases.įraud detection and prevention were also important. To create one-of-a-kind, siloed models, a variety of technologies were used. The prior big data infrastructure employed by Regions served as an operational data store with scattered data and no centralization. To accomplish this, Regions needed to become a best-in-class data-driven organization capable of delivering measurable results by integrating both internal and external data and employing advanced analytics, which would necessitate the right organizational structure, talent, and a data science platform capable of building analytical solutions at scale. Regions began its big data journey to better serve consumers, satisfy their needs, and assist them in reaching their financial goals. Regions serve customers in the South, Midwest, and Texas, and operates more than 1,300 banking facilities and 2,000 ATMs through its subsidiary, Regions Bank.

Regions Financial Corporation is a full-service provider of consumer and business banking, wealth management, and mortgage products and services in the United States. Using data science and machine learning to improve banking and wealth management. Personal banking services provided by Region Bank include savings, checking, and mortgage accounts. “Regions online Banking” provides a service consumer banking service. Pay bills, transfer money, manage your account and do other actions. Regions Online Banking makes it simple to bank safely and securely from any place.

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