Machine Learning Helps Agencies Sniff Out Fraud
“We can help identify which returns are the ones you should go audit based on peer-group analysis,” Hoehne says. “When you add machine learning, the process is more precise.”
Supervised ML, like in IBM’s Watson Studio, makes fraud detection possible by learning from past experiences.
“Supervised machine learning is based on the premise that you have known outcomes,” Hoehne says. “You can use these modeling techniques to create statistical representations to find which one identifies known outcomes.”
AI Delivers Significant Value to Agencies in Detecting Fraud
Other tools, such as AutoAI, Data Refinery and low-code visual modeling, help data scientists automate tasks and business users simplify modeling, says Ritu Jyoti, IDC’s group vice president for worldwide AI and automation research.
With a rise in grant fraud, in which grant recipients make false statements on their applications, forge documents and misuse money, users should see acceleration of AI-powered fraud detection solutions, Jyoti predicts.
“By some accounts, when executed properly, AI fraud detection systems can reduce fraud significantly while also reducing the costs associated with detecting them,” Jyoti says. “Results like these provide significant value for government agencies.”
As they start using AI technology for fraud detection, states should start with smaller-scale pilot projects to make sure their algorithms work, West advises. Later, they can scale up from that point.
“Agencies are trying to figure out how to track those transactions and make sure everything is done according to the letter of the law,” West says. “So, I think there will be a large expansion of the use of algorithms for fraud detection at the state level.”