# Technical price prediction
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/tech-prices.html

> Leverage historical insurance claim data for modeling and analysis.

# Technical price prediction

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/insurance_pricing/Code%20first%20Incurred%20Claims%20-%20Insurance%20pricing.ipynb)

This accelerator serves as a comprehensive guide to insurance pricing, leveraging historical claims data for modeling and analysis. The primary objective of this notebook is to enable insurance professionals and data scientists to predict insurance pricing accurately and efficiently with DataRobot platform.

This accelerator does the following:

- Set up the environment for insurance pricing modeling
- Import the necessary libraries and emphasize data preparation for use with DataRobot
- Visualize the distribution of claim amounts
- Create two options for modeling workflows for the insurance pricing project: Pure Premium vs. Frequency and Severity
- Explore different feature list and model customization

Following the modeling phases, the accelerator transitions to result analysis and business considerations. It discusses testing the models and computing various business metrics and scenarios. The accelerator also covers how to convert from a technical price to a market premium with the inclusion of fixed expenses and variable costs. This part also includes computing loss ratios by various segments, which is crucial for assessing risk and profitability. Finally, the analysis phase includes a dislocation premium chart to visualize premium impact.

After thorough analysis and fine-tuning, the accelerator explains how to deploy developed models into production. This is a critical step for implementing the insurance pricing models in real-world scenarios and utilizing them for decision-making.

The final section of the accelerator is dedicated to advanced workflows. It introduces feature discovery using secondary claims databases. This advanced approach can significantly reduce the time investment needed from data scientists and engineers, making it a valuable addition to the insurance pricing modeling process.

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