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Over the last five years, the number of identity thefts reported to the Federal Trade Commission has been steadily rising. In 2021, there were 22% more cases of identity theft reported than in 2020, racking up $1.2 billion in losses. Identity theft comes in many forms, including government benefits fraud, credit card fraud, loan fraud, bank account fraud, tax-related fraud, phone or utility fraud, etc.

Securing personal information can help prevent identity fraud. But that’s easier said than done. Banks, card issuers, and government need to take steps to combat identity fraud. Considering vast volumes and velocity, it would not be possible without automation, and AI/ML comes as a natural choice.


NEMESIS’s Machine learning technology can make more precise predictive solutions, stopping identity thefts before the next act. Users can leverage NEMESIS’s integrated Case Management System to manage investigative and resolve fraudulent activities.

No matter how complex the data is, NEMESIS provides automated data cleansing and preparation with a simple drag-and-drop feature. Users can select from a range of built-in models for enabling and experimenting. Without leaving the platform, users can customize dashboards with a variety of charts and graphs.


In classifying fraudulent activity, it is essential to provide labeled data, i.e., to create a variable indicating to which class (1 for fraudulent activity or 0 for otherwise) the given activity belongs t0. Other data include geo-location information, time parameters, channel, credit card limit, etc. Additional predictors are provided by banks, card companies, or government agencies.

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