While nearly every business vertical can benefit from the application of advanced data techniques and solutions, few can deliver as many advantages in visible ways to retail customers as financial institutions. From credit decisioning and account management to fraud prevention and cybersecurity, AI and data analytics are optimizing all aspects of banking and financial services and protecting their customers’ hard-earned savings.

Whether data solutions are being implemented for back office or customer-facing use cases, end users in financial services can realize worthwhile ROI if they invest and execute wisely. We have assembled several case studies and reports below that illustrate how some financial institutions have been using data science to improve different aspects of their operations and how that translates into benefits for their customers.

• Fraud prevention and anti-money laundering (AML) compliance. Fraud costs businesses and consumers trillions  of dollars globally each year. But the costs to businesses go beyond the direct cost of the fraud loss. Reputational damage causes customers to flee to competitors.  A complex network of rules—each specific to jurisdictions in which financial services providers are located and operate—tries to address the problem, creating compliance challenges in addition to fraud costs.  This case study from Starburst examines how improved data management can help banks monitor hundreds of millions of transactions each day to prevent fraud and financial crime.
• Deliver new online banking features faster and expand to new markets. Open-source enterprise software provider Red Hat worked with a commercial bank based in the African country of Mauritius to modernize its technology to include data containers and process automation. This case study details how Red Hat and MCB worked together to implement data-leveraging technology that saved its IT department thousands of work days per year and enabled faster deployment through a modernized core platform.
• Streamline data management to provide organization-wide visibility into customers. OTP Bank based in Hungary was searching for ways to work with its far-flung data sets and link social media and other data to its existing data warehouse to have access to better business intelligence. This case study highlights how the bank worked with Dremio to consolidate and streamline data management to achieve better results for customers.
• Commit to cybersecurity. While protecting data for all organizations is critical, for financial institutions that are entrusted with consumers’ life savings it’s even more important. But the nature and scale of data and how it is used is making legacy cybersecurity obsolete. A report from Databricks details how the challenges associated with securing data have morphed and how data lakehouses must be part of financial institutions’ cybersecurity posture.