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Home > AI for Insurance > Personalized Policy Pricing

Personalized Policy Pricing

Predict the most suitable Insurance Policy Pricing for each customer based on different factors including their personal information, medical history, their willingness to pay, competitor pricing, and other variables. Optimize Sales Volumes and Profit Levels

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Data Needed

1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Prudential Life Insurance Assessment

59,381 life insurance applications with the assigned risk rating. Each application consists of 126 features that are either continuous, discrete, or categorical(e.g. age, height, weight, BMI, family history, and more)

French Motor Claims Dataset

Kaggle Challenge + Data: Predict how often a driver will file an insurance claim in a year, the data contains risk features and claim numbers collected for 677,991 motor liability policies.

Medical Cost Personal Dataset

Kaggle Challenge + Data: Predict insurance costs using historical claims data including age, sex, BMI, smoking, region, and more


Off-the-Shelf Products using AI to Personalize Policy Pricing

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