Fraud or not Fraud? That is the question. Using modern Machine Learning techniques to identify fraud in credit card transactions.

Fraud or not Fraud? That is the question. In the talk, we will outline modern techniques on how to identify additional fraudulent transactions on Big Data and compare this to traditional fraud detection through a fictional credit card company, FraudX. In this talk, we will examine credit card fraud and how machine learning on Big Data can flag future fraudulent transactions. We will show how to select the “best models” based on multiple metrics like the associated cost of fraud, area under the ROC curve etc. We will compare machine learning based fraud detection to traditional fraud detection methods, translating the benefit into cost savings. We will also outline state of the art techniques that can be used to automatically obtain the best model given the data and the objective function without going through a series of tuning runs, truly democratizing data science and machine learning.

Nitesh is the Head of Data Science at Affirm. In this current role, he is responsible for all the core modeling that runs the decisioning at Affirm, including identity, anti-fraud, credit, and personalization. Nitesh has over ten years of experience in analytics and machine learning with specific expertise in recommendation systems, pricing models, and targeted advertising. Nitesh obtained his PhD in mathematical finance where he applied modeling techniques to stock and options data. He is also passionate about explainable AI and data science for social good.