Decisioning system in FinTech

Fintech companies have to process multiple decisions in real time or near real time. These online decisions directly impact the magnitude and flow of money. In my talk, I will describe how decisioning works in such an environment. At a high level, I will explain the decisioning system evaluates the likelihood of fraud and credit default and how it can be viewed as an amalgamation of rule based decisioning, machine learning and more specifically active learning. Unlike most other applications, this decisioning system does not get instant feedback on the decisions it makes. Moreover, the decisioning impacts what future iterations of the system can learn upon. In this talk, I will explain how to get around these problems and emphasize how we can do so in an interpretable, transparent fashion.

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.