The analytical lifecycle is iterative and interactive in nature. The process is not a one and done exercise, insurance companies need to continuously evaluate and manage its growing model portfolio. In the last of four articles on the analytical lifecycle, this blog will cover the model management process.

Model management is a critical step in the analytics lifecycle. It involves validating a model, selecting a champion model, and monitoring the performance of your models on constant basis to decide whether to retire upgrade current models or create new ones.

Let’s explore some important best practices for model management:

Model Validation and Comparison
With advances in modeling software and computing performance, it’s both easy and feasible to build multiple candidate models for use in analyses. By experimenting with different binning definitions, analysts can efficiently select alternate input variables, model techniques, and build multiple models. But before using these models, insurance companies need to evaluate them to determine which ones provide the greatest benefit.

 

Model Monitoring
To be effective over time analytical models need to be monitored regularly and adjusted to optimize their performance. Outdated, poorly performing models can be dangerous, as they can generate inaccurate projections that could lead to poor business decisions. As a best practice, once a model is in a production environment and being executed at regular intervals, the champion model should be centrally monitored through a variety of reports, as its performance will degrade over time. When performance degradation hits a certain threshold, the model should be replaced with a new model that has been recalibrated or rebuilt.

Model Governance
For insurance companies, greater regulatory scrutiny increases the need for model transparency. However, keeping records of the entire modeling process – from data preparation through to model performance – can be time consuming and unproductive. As a best practice, deploy model governance tools that enable you to automate model governance – complete with documentation to create qualitative notes, summaries and detailed explanations, as needed. Having an efficient model governance approval process can also save you time and money, as it can help insurers avoid audits and regulatory fines

To learn more about the model management process, download the white paper “Manage the analytical lifecycle for continuous innovation”.

I’m Stuart Rose, Global Insurance Marketing Director at SAS. For further discussions, connect with me on LinkedIn and Twitter.