Solving FinServ Problems Using ML Models Which Are Explained (Cloud Next '19)

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Financial services companies using machine learning models to solve critical use cases demand explainability with regulators. Learn how we solved financial services business-critical problems such as credit card fraud, anti-money laundering, and lending risk, using complex machine-learning models that can be explained to the regulators. The solutions are built on Google Cloud Platform, using Cloud Machine Learning Engine with complex deep-learning models. We demonstrate how we improved accuracy while reducing false positives while explaining these models to the regulators. Additionally, we converted these models to equivalent rule engines that can be used to augment existing rule-based solutions.

Solving FinServ Problems →http://bit.ly/2TSN2El

Watch more:
Next ’19 ML & AI Sessions here → https://bit.ly/Next19MLandAI
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions

Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform

Speaker(s): Chanchal Chatterjee, Adam Hammond

Session ID: MLAI107
product:Cloud ML Engine,AI Hub,Kubeflow,AI,TensorFlow; fullname:Chanchal Chatterjee;


Duration: 38:51
Publisher: Google Cloud
You can watch this video also at the source.