According to the Global Economic Crime and Fraud Report conducted by the global audit firm PwC, financial fraud and cybercrime hit an all-time high this year. In fact, in the past two years, 49% of international organizations reported experiencing economic fraud. While numerous institutions are introducing new technologies to eradicate crime, technology to prevent economic crime and fraud with artificial intelligence is attracting attention. According to a report by PwC, there are three common types of economic crime and fraud. These are asset theft, cybercrime, and consumer fraud.

In collaboration with BigML partner, INFORM Gmbh, we're pleased to bring the BigML community a new educational webinar: Machine Learning Fights Financial Crime. This FREE virtual event will take place on October 28, 2020, at 8:00 AM PDT / 9:00 AM PDT and it's the ideal learning opportunity for Financial institutions, banking sector professionals, credit professionals, risk advisers, crime fighters, fraud professionals, and anyone interested in finding out about the latest financial crime-fighting and risk analysis strategies and trends. Financial institutions must innovate to stop the onslaught of fraudulent transactions. The utilization of Machine Learning as a tool for fraud detection is trending. Combining Machine Learning with existing intelligent and dynamic rule sets produces a sustainable strategy to address this challenge.

With over 15 years of experience in Fraud and AML, and over 25 years of experience in financial services, Dan has helped countless customers solve their financial crime challenges in banking, insurance and government. Dan started out his career in IT, then moved to the business side to help business units implement technology. He then moved into Financial Crimes when consulting at TD Bank on their AML implementation in 2003. From there, Dan joined SAS and was the Fraud lead for Canada. Dan departed SAS for 5 years and gained further experience with TransUnion as the Director of Fraud and ID management.

Detection of fraud, waste, and abuse (FWA) is an important yet difficult problem. In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets. Each healthcare dataset is viewed as a heterogeneous network of patients, doctors, pharmacies, and other entities. These networks can be large, with millions of patients, hundreds of thousands of doctors, and tens of thousands of pharmacies, for example. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous networks within the overall graph structure. The system has been deployed on multiple sites and data sets, both government and commercial, to facilitate the work of FWA investigation analysts.