Improve Improper Payments Analytics Using Google Cloud

With states experiencing an overwhelming number of Unemployment Insurance Claims and Pandemic Unemployment Assistance Claims, it's critical to have the correct systems in place to properly process those claims. Federal, state, and local government agencies must ensure that improper payments and fraudulent claims are flagged, assistance reaches the correct citizens in a timely manner, and agents are not overloaded with manual claims processes.

 

Join Google Cloud, SpringML, and Carahsoft to learn about SpringML's Google Cloud-based solution to help you boost accuracy, improve efficiency, and minimize friction when processing social services claims. This solution uses machine learning to help identify patterns in the claims, uses AI-based algorithms to detect abnormal activities, and can be up and running in just 4-6 weeks.

• How to ingest and secure a massive amount of UI claims data in Google Cloud and why moving to the Cloud is critical for handling the next unexpected spike in claims without having to add more resources
• An end-to-end look at the claims process and where to build in configurable flags and pattern detection algorithms
• How to use AI-based verification to detect/confirm suspected fraudulent activity, reducing your chances of issuing improper payments and ensuring that minor mistakes don't result in major processing delays
• Ways to reduce manual workload and conserve resources by automating the improper payment detection process, allowing your investigators to focus on seamlessly processing verified claims
• The project implementation timeline for getting a solution in place in as soon as 4-6 weeks