Insurance

AI Adoption and the Insurance Industry

Today’s insurance companies are embracing AI and using more data science across different applications. In the process, these companies are improving profitability, becoming more efficient, and delivering an enhanced customer experience.

Request a Demo Download the Overview

AI and Insurance

DataRobot provides insurers with unrivaled power to optimize their market selection, underwriting, pricing, and claims management operations. Using DataRobot in insurance enables you 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.

Strategic Risk Selection

Identify the most profitable prospects
Accelerate conversion rates
Improve quote accuracy
Increase renewals while reducing “churn”
Inculcate “best practices”

Precision Pricing and Reserving

Access leading-edge, machine learning algorithms
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”

Optimized Claims Management

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

Insurance Case Studies

There is no function in insurance that will be unaffected by the adoption of artificial intelligence and machine learning. Besides automating and informing traditional processes, AI and machine learning create new capabilities that empower insurers to optimize every function in the insurance value chain.

Check out all Insurance use cases

Dynamic Pricing Precision

Using DataRobot for pricing machine learning, a large UK motor insurance carrier substituted a gradient boosting model for a generalized linear model (GLM) in one line of business. As a result, the carrier reduced its losses, improved its combined ratio, increased its retention rates, and reduced its acquisition costs. These improvements resulted in $8 million in savings. DataRobot’s ability to execute linear and nonlinear algorithms simultaneously helps deliver precise, risk-specific pricing that reduces vulnerability to adverse selection.
Reduced Churn
Mitigating Litigation Risk
Fraud Detection
Capitalizing on Subrogation

DataRobot Helps Insurers With:

Clear communication

DataRobot’s AI Cloud platform is designed for users to understand and explain predictions to customers, executives, and regulators. Factors with predictive value are clearly identified and explained, and “prediction explanation” codes tell users why an applicant received a certain price, score, or recommendation.
Easy platform integration
DataRobot provides the capability for straight-through deployment of analytical models, avoiding the need to reprogram. Users have several options for accessing our AI Cloud platform:
- Applications can interact with DataRobot models directly through the REST API;
- DataRobot can export models in Java .jar
- DataRobot can generate Java or Python scoring source code
- DataRobot can generate a scoring application with its own web-based user interface
“Democratized” modeling
Managing the lifecycle
Adaptable to “real world” data

“DataRobot gives you all the tools you need. It will democratize machine learning across the whole business.”

Pardeep Bassi
Head of Data Science, LV=
“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 behavior 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.”

Paul Davies
Head of Data Science, Domestic & General