Machine Learning in Insurance: Paving the Way towards Resilience

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Machine Learning in Insurance Paving the Way
towards Resilience
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Introduction Potential of Machine Learning in
Insurance Is Your Business Ready for ML Adoption?
Questions to Ask Before ML Deployment Benefits
of Scaling Machine Learning Concluding Thoughts
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Table of Contents
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INTRODUCTION
Driven by the accelerating digital
transformation, the insurance industry is
adopting innovative technologies and solutions
like artificial intelligence, machine learning,
data analytics, etc to stay ahead of the
competition.
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POTENTIAL OF MACHINE LEARNING IN INSURANCE
There are a number of real-world use cases of
machine learning in insurance. It helps insurance
providers understand risk appetite, detect fraud,
automate processes, improve underwriting, offer
personalized services, and more.
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IS YOUR BUSINESS READY FOR ML ADOPTION?
In order to successfully deploy machine learning,
you have to assess your ML readiness. With the
right strategies and tools, you can easily
overcome the challenges that come with machine
learning deployment.
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QUESTIONS TO ASK BEFORE ML DEPLOYMENT

• What problems need to be resolved with machine
  learning?
• At which level of readiness do you currently
  stand?
• Do you have access to quality data?
• What are your targeted processes?
• Are there clear KPIs for processes?

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BENEFITS OF SCALING MACHINE LEARNING
In order to reap the most out of machine
learning, insurance companies should scale ML in
operational processes throughout the enterprise.
It helps them offer better customer support,
improve risk assessment, and boost productivity.
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CONCLUDING THOUGHTS
Machine learning in insurance extracts insights
from data and helps insurers make data-driven
decisions. It helps insurance companies enhance
business scalability and improve business
operations.
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