Performing Transaction Synthesis through Machine Learning ModelsPublic
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ACI Worldwide is a payment processing company that uses fraud detection solutions to process the massive amount of transactions that go through the company every day. The goal of this MQP project was to address privacy concerns in using real transaction data to test fraud detection software. We worked with our advisors at WPI and ACI to develop a product that can be used by third party companies to test their fraud detection solutions. Our team looked at different machine learning and statistical methods to build working models from the large quantities of transactional data and then use those models to synthesize artificial data that follow the same patterns and behaviors. Our team also developed a test suite to measure the accuracy of and validate the generated data.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
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