Kuidas uuendada riskimudelit uute andmete ja hüpoteeside ilmumisel?

Updating the risk model when new data and hypotheses appear is an iterative process that involves several key steps:

1. Collection and preparation of new data — integrating fresh data, cleaning, and transforming it to match the model format.

2. Analysis of new hypotheses — verifying new assumptions that may influence the features or structure of the model.

3. Training or retraining the model:

   • If the model supports incremental learning, it can be retrained on the new data.
   • Otherwise, retrain the model with a combined dataset.

4. Validation and testing — assessing the quality of the model on reserved data, checking risk metrics.

5. Deployment and monitoring — deploying the updated model into production and monitoring its performance.

6. Automation — automating the update process as much as possible to respond quickly to changes.

Example: if a new hypothesis about the influence of an additional feature on risk arises, this feature should be added to the data, the model retrained, and its accuracy and stability checked to see if they have improved.