Tag: fraud detection

At California Community Colleges (CCC), administrators estimate that nearly one in five applications is fraudulent. That creates a difficult choice for the college system, whose core mission is to make higher education as accessible as possible. To intelligently and thoughtfully flag fraud at scale without restricting students’ ability to apply, the CCC turned to Amazon Web Services (AWS) and AWS partner, Pariveda Solutions, to develop a solution. In this blog post, you will learn how CCC and Pariveda built a machine learning (ML)-powered fraud detection and prevention solution that helps identify and manage fraudulent accounts before they reach the campuses.

To gain operational efficiencies and reduce workload burdens on employees, some state finance and tax agencies are leveraging robotic process automation (RPA) on AWS. RPA is a software tool that integrates with almost any system or application and performs manual, repetitive, time-consuming tasks. Tax agencies can use AI and ML to support the sheer size and scale of data they manage and to access and analyze all types of data with ease, including voice, video, and streaming data. Find out three ways AI and ML are creating measurable outcomes for tax agencies.

To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.