Machine Learning and Automation: Staying on Track to Detect Fraud, Waste and Abuse
To provide proper oversight for government funding including the unprecedented CARES Act, federal agencies will be able to rely on data analytics and deep learning methods like never before. With advancements in machine learning combined with automation best practices, agencies will be able to gain effectiveness in the detection and prevention of fraud, waste, and abuse while providing transparency across government and the public. The use of these technologies can be leveraged to manage the unparalleled scale and complexity as well as the vast amounts of data required for oversight – and will reshape the ability of the government and the governing bodies which are overseeing the funding.
The webinar will focus on the best practices that will support the oversight of the CARES Act as a way to illustrate opportunities for all agencies to transform.
- Discuss how the convergence of today’s technologies enable efficient monitoring, prevention, and recovery of funds – eliminating errors with increased visibility and preserving program integrity with accurate, actionable data
- Highlight multi-sectoral use cases in detecting fraudulent trends and patterns through predictive analytics and machine learning
- Examine the process of data ingestion into machine learning processes and the use of deep learning methods
- Share automation best practices that help improve speed and accuracy in fighting fraud, waste, and abuse
- Kent Cunningham, CTO, Microsoft (confirmed)
- Mike Wood, Executive Director, Recovery Board (confirmed)
- Gaurav "GP" Pal, CEO and Founder, Stack Armor (confirmed)
- Amit Khare, VP, Consulting Services, CGI (confirmed)
- Brady Kline, GeoAL/ML Expert, Esri (confirmed)
- Jason Porter, VP and Emerging Technologies Practice Lead, CGI