Machine Learning in the Public Sector

The public sector is using automated machine learning to reduce the time and level of expertise needed for agencies to move into artificial intelligence. Automation delivers the power of machine learning to the domain experts who need it without needing to learn a programming language or hire cost-prohibitive data scientists. By using automation machine learning, agencies can more effectively serve their citizens in important areas such as safety, health, fraud, defense, justice, and public services. Our many uses case examples in these areas demonstrate the power of DataRobot.

Use cases in Public Sector

Counterterrorism

Predicting and preventing terrorist attacks is a chief concern for intelligence and agencies, and predictive modeling based on historical data may help prevent them in the future.

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Cybersecurity

Cybersecurity is emerging as one of the greatest threats of the future, and federal agencies are particularly vulnerable. Build, deploy and refresh models to predict incoming threats in real-time.

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Fraud detection

Almost every government agency serving the nation's citizens suffers from fraud, costing approximately $80 billion a year. Data analysis and predictive modeling can combat this issue in minutes, not months.

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Insider threat

Threats can come from all sides, not just externally but from inside government agencies as well. These agencies need to proactively block any potential misuse, using machine learning to identify exploitation of inside information.

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