Developing Credit Scoring Models for Banking and Beyond

Interpretability of machine learning models is paramount in many industries from credit-worthiness to insurance claims, anti-money laundering to readmittance to a hospital.

Interpretability promotes understanding, and understanding promotes responsible use of analytical models.

Data science is meant to be a tool to aid people's work, not a replacement for people's work.

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