Machine learning helps agencies sniff out fraud
Richard Hoehne, leader of IBM’s Worldwide Financial Crimes, says the company is working with the state to address tax evasion using machine learning (ML).
According to Hoehne, it helps identify which returns need to be audited based on peer group analysis. Adding machine learning makes the process more accurate.
Supervised ML, like Watson Studio in IBM, enables fraud detection by learning from past experience.
Supervised machine learning is based on the premise that the results are known, Hoehne says. You can use these modeling techniques to create statistical representations and find one that identifies known results.
Related: How is the use of artificial intelligence accelerating in the state government?
AI brings great value to agencies in fraud detection
Other tools such as AutoAI, data refinery, and low-code visual modeling help data scientists automate tasks and simplify modeling for business users, and IDC’s Global AI and Automation Research Group Vice President. Said Ritu Jyoti.
With the rise of grant fraud, where grant recipients make false statements about applications, forge documents, and misuse money, users should see an acceleration in AI-powered fraud detection solutions. Is predicting.
For some accounts, properly running an AI fraud detection system can significantly reduce fraud while reducing the costs associated with fraud detection, Jyoti said. Such results bring great value to government agencies.
West advises that once you start using AI technology for fraud detection, the state needs to start with a small pilot project to make sure the algorithm works. You can scale up from that point later.
The agency is trying to find a way to track these transactions and make sure everything is done in accordance with the provisions of the law, West said. Therefore, I think the use of fraud detection algorithms at the state level will be significantly expanded.
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