Kwan Lin

Kwan Lin

Principal Data Scientist at MoonPay

    Kwan is a Principal Data Scientist at MoonPay. On a day to day basis, he is involved with training models, contributing to the model deployment infrastructure, providing support to maintain the persistent availability of the models, and coordinating with stakeholders across the business. His major focus now is on fraud prevention. Prior to joining MoonPay, Kwan was a data scientist at a behavioral biometrics startup, a cybersecurity company, and a data analytics company. Kwan holds degrees in international security from the Fletcher School at Tufts University and economics and international relations from Brown University.

    All Sessions by Kwan Lin

    Day 3 04/25/2024
    1:00 pm - 1:10 pm

    Practical Considerations for Machine Learning in Fraud Prevention Programs

    <span class="etn-schedule-location"> <span class="firstfocus">ML Safety & Security</span> </span>

    In this presentation, we will walk through in general terms how machine learning has been utilized in a financial technology company in the web3 space to detect and prevent fraud. We will cover the particular considerations of fraud as it relates to building and deploying effective machine learning models. We will lightly delve into the code and tooling to illustrate from a practical standpoint how the machine learning system can be constructed. At the end of the presentation, the audience should have a conceptual understanding of how a machine learning program can be implemented to prevent fraud. The effective application of machine learning to detecting fraud does require significant nuance and measured consideration as there are unique attributes to fraud that are not present in other domains, including: the delayed availability of credible labels for fraud (sometimes fraud labels are not available for months after particular events, if ever), variations of the types of fraud that might manifest (Is it first party fraud or third party fraud? Is it truly fraud, or is it merely unfavorable customer behavior?), and considerations around timeliness to be able to prevent fraud rather than to merely react to fraud after the fact, at which point recourse options for a business might be limited. In addition to the domain concerns of how machine learning fits into a broader fraud prevention program, there are practical considerations extending from how fraud detection models are trained, to the machine learning ops considerations that are necessary to serve and maintain timely and reliable models, and through to the support mechanisms necessary to ensure that machine learning is persistently available as a business-critical function.

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