Most analytics applications are built for regular data and normal processes—handling about 99% of use cases. But at business scale, the remaining 1% of irregular or anomalous cases can translate to millions or billions of data elements—each potentially connected to other data sets. This mixing of regular and irregular data can be a serious problem for machine learning or AI models that only expect to process typical data. But an opportunity hides here as well.

Blog post from Elder Research discusses using analytics to help reduce overpayments and underpayments in unemployment insurance claims and reduce fraud, waste, and abuse for the U.S. Department of Labor Unemployment Insurance Integrity Center of Excellence.