Financial fraud is a major problem that costs businesses and individuals billions of dollars each year. In recent years, fraudsters have become increasingly sophisticated in their use of technology, making it more difficult for banks to detect fraud. Attend this use case session to learn how to utilize RPA, data analytics, data mining, and a variety of automation tools to detect and prevent financial fraud.

In this session, topics of discussion will include, yet will not be limited to:

• How RPA can automate repetitive tasks, such as collecting data from different sources and entering it into a data analytics tool
• Data analytics tools that enable finance to analyze large amounts of data, so to identify patterns and anomalies
• Data mining to identify patterns and trends in large datasets of customer transactions
• Machine learning to identify historical data of fraudulent and non-fraudulent transactions

IA Maturity Level: All & Various

Lines of Business Functional Focus: Finance, Operations, Technology & Much More