Mar 15–18, 2020

Solving financial services machine learning problems with explainable ML models

1:45pm2:25pm Tuesday, March 17, 2020
Location: 210 F

Who is this presentation for?

  • CXOs, senior management, and regulators




Financial services companies use machine learning models to solve critical business use cases. Regulators demand explainability of these models. Chanchal Chatterjee shares how Google solved financial services business critical problems such as credit card fraud, anti-money laundering, lending risk, and insurance loss using complex machine learning models that you can explained to regulators.

The solutions are built on the Google Cloud Platform using the Cloud AI Platform with complex deep learning models. Chanchal demonstrates how Google improved accuracy and reduced false positives while explaining these models to the regulators. Additionally, the company converted these models to equivalent rule engines that can be used to augment existing rule-based solutions.

Prerequisite knowledge

  • General knowledge about machine learning and artificial intelligence

What you'll learn

  • Understand why you should use model explanations for financial services regulators
  • Identify methods of model explanation in financial services and types of explanations (for regulators)
  • Discover explanations for financial services use cases, lending risk, and insurance loss prevention example
  • Learn how to create augmented rules out of explanations
Photo of Chanchal Chatterjee

Chanchal Chatterjee


Chanchal Chatterjee is a cloud AI leader at Google Cloud Platform with a focus on financial services and energy market verticals. He’s held several leadership roles focusing on machine learning, deep learning, and real-time analytics. Previously, he was chief architect of EMC at the CTO office, where he led end-to-end deep learning and machine learning solutions for data centers, smart buildings, and smart manufacturing for leading customers; was instrumental in the Industrial Internet Consortium, where he published an AI framework for large enterprises. Chanchal received several awards, including the Outstanding Paper Award from IEEE Neural Network Council for adaptive learning algorithms recommended by MIT professor Marvin Minsky. Chanchal founded two tech startups between 2008 and 2013. He has 29 granted or pending patents and over 30 publications. Chanchal earned MS and PhD degrees in electrical and computer engineering from Purdue University.

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