How is AI changing the Insurance industry?

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AI Adoption and the Insurance Industry

Are Insurers already applying AI?

AI has the potential for transformative changes in the Insurance industry, it will reset business processes for making decisions in property, casualty, and life.

Using AI & machine learning enables traditional insurers to deploy analytics in a fraction of the time it usually takes, delivering enhanced speed to market, more accurate pricing, reduced loss ratios, and higher conversion rates all whilst providing resilience in these uncertain times.

87% of insurance companies have already invested over £5 Million in AI to disrupt this market, let us show you how we can help – Everest Global.

0of insurance companies have already invested over £5 Million in AI to disrupt this market, let us show you how we can help – Everest Global

AI and Insurance

AI and Insurance

AI-driven insurers can deploy predictive analytics in a fraction of the time it usually takes. Our Automated Machine Learning platform, delivers a competitive advantage that provides vastly greater speed to market, more accurate pricing, reduced loss ratios, and higher conversion rates.​

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Challenge 1
Solution 1
Strategic Risk Selection
Strategic Risk Selection
Insurers need to deploy analytics in a fraction of the time it usually takes, delivering enhanced speed to market, more accurate machine learning pricing, reduced loss ratios, and higher conversion rates to minimise risk.

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  • Identify: profitable prospects
  • Accelerate: conversion rates
  • Improve: quote accuracy
  • Increase: renewals and reduce “churn”
  • Inculcate: “best practices”

See Challenge

Challenge 2
Solution 2
Precision Pricing & Reserving
Precision Pricing & Reserving
Insurers must improve the precision of pricing and reserving to reduce its losses, improve its combined ratio, increase its retention rates, and reduce its acquisition costs.

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  • Deploy pricing: models without reprogramming
  • Increase accuracy: of loss costs
  • Develop rates: five to fifteen times faster
  • Develop losses: individually for each claim
  • Build reserves: accurately from the “bottom up”
  • Access leading-edge: machine learning algorithms

See Challenge

Challenge 3
Solution 3
Optimised Claims Management
Optimised Claims Management
AI and machine learning create new capabilities that empower insurers to optimize every function in the insurance value chain.

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  • Identify claims: for straight-through or manual processing
  • Flag potentially: fraudulent claims
  • Identify subrogation: opportunities
  • Predict claim severities: and large loss potentials
  • Improve adjuster performance: with outcome-based assignments

See Challenge

All use cases underpin increasing revenue, reducing costs and delivering excellent customer service

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High value use cases for every Insurer

Dynamic Pricing Precision

Reduced Churn

Mitigating Litigation Risk

Fraud Detection

Capitalising on Subrogation

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AI Success Stories

  • 01 / 04

    “I’d reckon we’ve gained at least one point savings on the loss ratio, so you’re looking at $7 million pound savings”
    Chief Actuary

  • 02 / 04

    Automate building an accurate model to predict the likelihood that a claim is fraudulent. With the results, you can create a rank-ordered queue of claims for fraud units to investigate.

  • 03 / 04

    “DataRobot gives you all the tools to you need to prevent churn. It will democratise machine learning across your business”
    Head of Data Science

  • 04 / 04

    “We want to be truly customer-focused with all our 16 million customers, and to do that we need to be able to predict the potential behaviour of each of them to put the right offer in front of them at the right time. There’s no way we can be as customer-focused as we would like without the help of machine learning”
    Head of Data Science