MACHINE LEARNING FOR FRAUD DETECTION
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United States of America
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Sep 21, 2017
app pub date -
Mar 15, 2016
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Mar 15, 2016
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Abstract
System, method and media for detecting fraud in submissions of tax return data. Machine learning techniques including cluster analysis and tree-based classifiers are used to analyze large volumes of previously submitted tax returns based on tax data and submission-related data to detect patterns in genuine and fraudulent returns. These patters are then used to generate rules that can be installed in fraud detection systems in real time to prevent the submission of fraudulent returns. Previous classifications and fraud scores of submitted returns can be updated based on new rules or external indications of fraud.
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