This document discusses how machine learning can be applied in various industries like insurance, financial services, and healthcare. It presents use cases of machine learning in areas like cost reduction, risk management, fraud detection, marketing, and improved customer service. It then describes the key aspects of automated machine learning as developing many models at scale, deploying them easily, and using the models for day-to-day business decisions. The document promotes an automated machine learning platform as making machine learning scalable, pervasive and inclusive.

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Automated Machine Learning
Gourab De, VP of Data Science Practice, DataRobot
Predictive Analytics World, San Francisco, 2017

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Machine Learning in Insurance business decisions
COST
MARKETING &
RISK SELECTION
QUOTING, UNDERWRITING &
AUDIT
CLAIMS
MANAGEMENT
PREDICTING FRAUD &
LITIGATION
RETENTION
REVENUE

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Machine Learning in Financial Services
COST
RISK STRATIFICATION
UNDERWRITING
LOAN MANAGEMENT FRAUD DETECTION
RETENTION, CROSS/UP-SELL
REVENUE
RISK AND
COMPLIANCE
ANTI-LAUNDERING
MARKETING

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Machine Learning in Healthcare Delivery
Improved service
● Predicting escalation/
de-escalation of care
● Predicting occupancy of ICU
● Optimizing staffing needed
for ED
Better outcomes Reduced cost
● Predicting hospital-acquired
infection
● Predicting postoperative
outcomes
● Risk stratification
● Predicting treatment
effectiveness
● Predicting avoidable
readmission
● Predicting patients who are
likely to sue
● Predicting delay in payment
● Fraud detection

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The Blueprint
1. Develop a lot of models
2. Deploy them all
3. Use them in decision making

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Point of Failures in Machine Learning
1. Hiring and retaining good data scientists is impossible
2. Targeted end-users of machine learning product are
not included in the problem solving
HBR Article you may find interesting

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New Technology Opens up New
Possibilities to the Executives
Artificial Intelligence & Automation
makes Machine Learning Scalable,
Pervasive and Inclusive

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What defines Automated Machine Learning
Accuracy open-source competitive model selection, built for iteration
Control appeals to advanced data scientists
Interpretability transparency to validators, actionable insights to business
Integration not a model built in a lab, ready for production

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Develop a lot of models … at scale

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Deploy them effortlessly ...

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Use them in day to day business decision

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Questions?
Gourab De
gourab.de@datarobot.com