Machine Learning in Insurance: Paving the Way towards Resilience
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Table of Contents
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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
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.
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.
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.
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?
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.
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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