Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Explainable machine learning in fintech

Eitan Anzenberg (Bill.com)
14:5515:35 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Average rating: ****.
(4.50, 4 ratings)

Who is this presentation for?

  • Data scientists, machine learning engineers, credit risk officers, and lending professionals

Level

Intermediate

Prerequisite knowledge

  • Familiarity with machine learning

What you'll learn

  • Understand how to interpret black box nonlinear machine learning algorithms at the prediction level

Description

As machine learning applications become more specialized, the models become increasingly opaque and harder to interpret. The ability to interpret black box nonlinear models is critical in certain fields such as finance, healthcare, and self-driving technology. Flowcast partners with several banks to leverage their proprietary data to build credit-risk models using machine learning, which helps unlock capital for small to medium businesses (SMB).

A credit decision requires both an accurate assessment of risk and plain English explanations. For example, it’s not enough to reject a potential client but to give a reason. Eitan Anzenberg explores a solution that utilizes what-if scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as locally interpretable model estimation (LIME). At each prediction, Flowcast calculates the marginal impact of each feature independently to the response variable. The solution compares this approach to standardized methods such as LIME and reports the computational efficiency and accuracy of explanations. Flowcast then develops an accompanying plain English explanation, such as, “Client A is rejected because their months since most recent diluted payment is 2 (1.8 above median), and the USD amount requested is $72K ($57K above median).” These explanations are required for compliance and help build trust with Flowcast’s banking partners’ credit risk officers.

Photo of Eitan Anzenberg

Eitan Anzenberg

Bill.com

Eitan is currently the Director of Data Science at Bill.com and has many years of experience as a scientist and researcher. His recent focus is in machine learning, deep learning, applied statistics and engineering. Before, Eitan was a Postdoctoral Scholar at Lawrence Berkeley National Lab, received his PhD in Physics from Boston University and his B.S. in Astrophysics from University of California Santa Cruz. Eitan has 2 patents and 11 publications to date and has spoken about data at various conferences around the world.