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

Generative AI has the potential to revolutionize industries and transform the way we live and work. However, with great power comes great responsibility - and the potential risks of generative AI cannot be ignored, most notably with respect to data governance, privacy, IP, bias, ethics, workforce usage, and much more. That said, before an organization can dive into the generative AI pool on a business level, it’s mission critical to better understand what the risks maybe, so to determine if it’s indeed a technology your organization or functional areas can use at scale, or if silo usage is best at this time.

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

• Rewards, benefits, and potential
• Risks – governance, oversight, audit, compliance

IA Maturity Level: Early Adoption & Various

Lines of Business Functional Focus: Technology, Operations & Business-Wide