- B
- Blockchain
- C
- Customer Churn
- Counterterrorism
- Cybersecurity in the Public Sector
- Credit Card Fraudulent Transactions
- Credit Default Rates
- Conversion Modeling
- Claim Payment Automation Modeling
- Claim Development Modeling
- D
- Drug Delivery Optimization
- Disease Propensity
- Digital Wealth Management
- Direct Marketing
- E
- Estimating Sepsis Risk
- F
- Finding Duplicate Customer Records in Your Database
- Fraud detection
- Finding New Oil and Gas Sources
- Fraudulent Claim Modeling
- G
- Google AdWords Bidding
- H
- Hospital Readmission Risk
- I
- Inventory Forecasting
- Insider Threat in Public Sector
- Insurance Pricing
- L
- Loyalty Program Usage
- Life Insurance Underwriting
- M
- Multichannel Marketing Attribution
- Modeling ICU Occupancy
- N
- Next Best Offer
- Next Best Action
- P
- Product Personalization
- Q
- Quality Assurance
- S
- Supply Chain Management
- View global site search results

Insurance Pricing
Problem: Getting Insurance Pricing Right is Tricky Business
Too high? Too low? Or just right? Indeed, AI can be your Goldilocks when it comes to insurance pricing.
To be profitable in the insurance industry, you must get the price right. Accurate pricing is key for an insurer to avoid being adversely selected against, leading to a healthier bottom line.
Insurers operate in a very competitive market. With the popularity of comparative raters in the insurance market, prospects can compare prices from many companies instantly and often will go with the lowest price. This undoubtedly puts less sophisticated insurance carriers at a disadvantage and makes them vulnerable to adverse selection especially in segments being mispriced. This results in deteriorating underwriting profits, which ultimately will negatively impact an insurer’s financial strength and its ability to fulfill its promise to various stakeholders.
Solution: Machine Learning Helps Get the Price Right
All insurance policies are good policies if they are priced right. An accurate pricing model relies on an insurer’s ability to find meaningful ways to refine its risk segmentations by evaluating all available attributes, identifying impactful interactions, and making nimble adjustments if needed.
Machine learning models are good at analyzing large amounts of data and identifying interactions among various attributes to achieve the most accuracy. With the increasingly high insurance cost and easy access to different carriers’ quotes, more and more insureds will shop insurance more frequently. This reality makes an accurate pricing model even more critical for an insurer in order to attract and retain the right customers and maintain a healthy bottom line.
Why DataRobot? AI Makes Modeling Quick and Easy
With DataRobot’s wealth of algorithms, you can be confident that the prices you charge are based on the most accurate model. By automating the many repetitive and time consuming tasks, DataRobot accelerates the model development process. In addition, DataRobot provides built-in insurance specific features, significantly reducing the time needed for rate filing.
Pricing based on the DataRobot AI Platform model is the way to get it just right the first time, every time.