How is AI changing the Insurance industry?

CLICK

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-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.

See how Data Technology can help you

Challenge 1

Solution 1

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.

See Solution

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

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.

See Solution

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

AI and machine learning create new capabilities that empower insurers to optimize every function in the insurance value chain.

See Solution

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

High value use cases for every Insurer

Dynamic Pricing Precision

Using Automated Machine Learning for pricing, a large UK motor insurance carrier substituted a gradient boosting model for its generalised linear model in one line of business. As a result, the carrier reduced its loss ratio, improved its combined ratio, increased its retention ratio, and reduced its acquisition costs — in all, adding value worth up to $8 million. Our platforms ability to execute linear and nonlinear algorithms simultaneously helps deliver precise risk-specific pricing that reduces vulnerability to adverse selection.distribution or store environment.

Reduced Churn

Insurers lose money when good customers don’t renew, as lapsed policies need to be replaced with more costly new business. Using Automated Machine Learning, a large European insurer incorporated the risk of “churn” into its renewal pricing, leading to reductions in cancellations and non-renewals, an improved loss ratio, and a 24% reduction in variable costs. In all, the company estimates the value at €12.5 million a year.

Mitigating Litigation Risk

A large commercial U.S. property and casualty carrier used Automated Machine Learning to develop a model for predicting the likelihood that a workers’ compensation claim would lead to litigation. Claims scoring high for the probability of litigation are referred to senior claims staff for early and attractive settlement offers. The company estimates that it has avoided 10% of the litigation it would have experienced without the model, leading to a 25% decrease in the cost of at-risk claims and an estimated value to the company of more than $5 million per year.

Fraud Detection

Modern machine learning is far more effective than static rules in detecting ever-evolving methods of fraud. In one case, a large European property and casualty insurer implemented overnight batch runs of auto claims against a model developed by using DataRobot. Claims scoring high for probability of fraud are now assigned to a specialised claims fraud investigation team. The company estimates that it has increased the accuracy of its fraud detection by 30%, yielding more than $10 million in value.

Capitalising on Subrogation

Subrogation opportunities are like finding money, but only if you can identify them and act quickly. A continental European motor insurer worked with DataRobot to identify claims with a high probability for subrogation recovery. Claims handlers now receive automated lists of claims with subrogation. The company has doubled its subrogation rate from 1.4% to 2.8% of claims and expects annual recoveries of €4-8 million a year.

Trusted By Global Leaders

Click below to see how we can help you

Chief Data Officers

Increase the productivity of your data science team.
With automated machine learning, you can get the productivity of a large data science team from a small one. Let DataRobot find the best models for you and use DataRobot’s simple deployment options to get them to market faster. Relieve data scientists from documentary requirements by using DataRobot’s automated model risk management and model validation templates.

Chief Technology Officers

Bring AI and machine learning-based solutions to market faster.
Get models to production faster using DataRobot’s low-risk model deployment options, including code generation, deployment to Spark, and API-based deployment capability.

Chief Data Scientists

Annihilate your backlog of analytics requests.
Let DataRobot suggest the best model in each situation, saving you the time and effort of trying and comparing every model. Use automated machine learning to build many models at the same time it took to build one, increasing precision with more model granularity. Let DataRobot handle the low-risk models from start to finish so you can focus your talent where the payoff (or the risk) is the greatest.

Chief Information Officers

Monetize your investments in data infrastructure.
The bottleneck in many banks is no longer a lack of data, it’s plenty of data but not enough analytics staff to turn that data into insight. Democratize data science with DataRobot and watch the performance of your business take off as the data reveals opportunities and improvements.

Chief Marketing Officers

Hyper-personalise your offers direct to consumers.
Research has shown that brands adopting the technology and strategies to enable personalised digital experience will gain competitive advantage. They find it easier to acquire customers, enable faster conversions and create lasting engagement and loyalty.

Chief Risk Officer

Replace outdated tools with AI that achieves better results with lower costs.
CRO’s spend millions attempting to detect, investigate, and report on potential money laundering and fraud. Our machine learning models use the bank’s data to learn from the bank’s own experience. These models can then be used to block fraud before it happens. This saves time; cuts down costs and resources, and can reduce the rate of false negatives down to nearly zero.

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