skip to main content

Introduction

This report provides a snapshot of machine learning adoption in the global insurance sector. It also examines the key investment priorities and use case for machine learning over the next 24 months.

Highlights

  • Overall, 12% of insurers are currently deploying ML within their organization compared to 17% across all industries. Wider adoption of ML by insurers is being hindered by regulatory issues.

Features and Benefits

  • Allows insurers to benchmark their machine learning strategy against their peers.
  • Analyzes trends in machine learning spending in the insurance industry.

Key questions answered

  • What are the key machine learning investment priorities for the insurance sector?
  • What are the key challenges of machine learning adoption within the insurance sector?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for insurers
  • Recommendations for vendors targeting the insurance sector

Insurers lag other verticals in the deployment of ML but are addressing the gap

  • Some insurance sectors currently lag many verticals in their deployment of ML
  • Regulation and data architectures are limiting ML adoption in the insurance industry
  • Accelerating the use of ML is a key priority for insurers

Fraud and digital channels are the most important use cases for machine learning in insurance

  • Improving loss ratios is the immediate use case for ML
  • ML is a critical element of insurers becoming fully "digital"

Insurers will use a range of machine learning technologies to deliver digital transformation

  • ML is being used to exploit the value of existing data across all insurance sectors
  • NLP and chatbots projects are a priority for the non-life sector
  • Life insurers are adopting intelligent RPA to streamline complex processes
  • Image recognition and analysis will become an important component of insurance

Appendix

  • Methodology
  • Further reading
  • Author