The way federal agencies make sense of data is changing drastically. And it stands to reason, right? Agencies are creating more data than ever before, and there are more tools at their disposal than ever before to make sense of it. Deriving value from data can transform the way agencies deliver services and, ultimately, positively impacting the people they serve.

Today, artificial intelligence, machine learning, natural language processing, computer vision, intelligent automation and cognitive agents are being used to support missions in entirely new ways. The following examples represent is a new way of using analytics to solve hard problems.

• Detecting fraudulent Medicare payments at CMS: Machine learning is identifying potentially fraudulent Medicare payments at the Centers for Medicare and Medicaid Services. In a single day, CMS receives millions of claims representing billions in payments. Our team leveraged machine learning alongside regression analysis, link analysis, outlier detection and behavioral analysis to identify risky providers, which has given CMS the ability to analyze large volumes of data that went previously unanalyzed.
• Classifying medical images for the VA: Working in partnership with the Department of Veterans Affairs, a machine learning-based classifier was developed to help physicians spot malignant skin lesions. We used computer vision and built deep learning image classification models trained on more than 10,000 images of skin lesions. Once field tested, primary care providers may be able to use the tool to more rapidly and accurately diagnose these conditions, many of which may have gone undetected as care was delayed during the pandemic.