Automatic and Dynamic Models Backtesting

Boris Fievet


use case iziday data data engineer big data machine learning deep learning ai ia

Banking and Insurance

Context and Challenge:

Our client in the insurance sector was constantly facing the challenge of accurately predicting and pricing for potential risks and losses. In order to do this effectively, insurance companies rely on models to analyze and forecast trends and patterns.

However, these models are only as accurate as the data they are based on and can become outdated quickly in a rapidly changing market. As a result, insurance companies must regularly backtest their models to ensure their accuracy and effectiveness.


To address this challenge, we implemented an automatic and dynamic backtesting system to continuously assess the performance of ou client models. This system uses real-time data and automated algorithms to compare the predicted results of the models with actual outcomes. The system also allows for the integration of new data and variables to continuously update and improve the models.


The implementation of the automatic and dynamic backtesting system resulted in a significant improvement in the accuracy and reliability of the insurance company's models. This led to more accurate risk assessments and pricing, resulting in increased profits and customer satisfaction. The system also allowed for more efficient and effective decision-making, as it provided timely and accurate data to inform business strategies. Overall, the implementation of the system resulted in a significant competitive advantage for the insurance company in the market.

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