This Guest article was written by Nickolas A. Alvarado, FCAS, CSPA, MAAA and Brett Nunes and first appear here
Empower data and risk management with unconstrained modeling
The insurance industry is experiencing a fundamental shift in how to define, understand, and quantify risk. Recent technological advancements have led to an explosion of data, demanding new processing and analysis techniques. Consequently, insurers face a new challenge: finding a balance between developing highly accurate models and complying with business and regulatory requirements.
Unconstrained models maximize data utility and predictive power by leveraging advanced algorithms. For organizations that embrace them, unconstrained models present an opportunity to improve risk management and gain a competitive advantage.
The industry standard for insurance analysis, modeling, and product development centers on the generalized linear model (GLM) and its variants, which have been used since the late 1980s. The effectiveness of traditional models like GLMs will always be limited by inherent constraints, conditions that restrict the scope of a model’s inputs and outputs to explain a given phenomenon or process. Constraints always exist due to natural or intuitive limits or boundaries but limit the power of models.
Model constraints can be grouped into one of these several broad categories:
Unconstrained models place no limits on data, models, or algorithms. They are designed to maximize predictive power and accuracy by identifying the most relevant data and selecting the optimal analytical framework. It’s important to view these models as strategic tools that complement other models, helping to gain insights, unlock value, and enhance data utility. By employing advanced algorithms, unconstrained models can effectively process large and diverse datasets.
Unconstrained models can provide quick insights through gap analysis, where constrained models are compared to a less constrained one that sets a performance ceiling. By examining the resulting performance gap, often visualized in a chart, we can identify the loss of predictive power in the constrained analytical framework. Understanding and minimizing this gap helps to maximize data insights and enhance the value of the model.
Figure 1 is a lift chart that illustrates the predictive power of two models: The dark blue model line has minimal constraints, while the light blue model line is constrained. The dark blue line is steeper from left to right than the light blue line, indicating the dark blue model is more powerful.
Alternatively, models are compared by examining the change in the area “under the (lift) curve.” Figure 1 illustrates the area difference in the grey region, representing the loss of predictive power. From a business perspective, this may represent loss of segmentation, fairness, profit, and value.
Figure 1: Lift chart to illustrate performance gap
Statistical modeling is both science and art, blending technical know-how with creative problem-solving. Unconstrained modeling requires an even higher level of skill to ensure the right methods achieve the desired outcomes.
A successful modeling project requires three essential ingredients:
Figure 2 shows example projects within a grid where the horizontal axis represents constraints and the vertical axis represents model flexibility. Insurance pricing falls in the bottom left (high constraints, low flexibility), whereas advertisement serving is in the top right (low constraints, high flexibility). Each project’s position on the grid is based on its expected constraints and flexibility and serves as a useful guide when starting a project.
Figure 2: Examples of constrained and unconstrained modeling projects
Model governance ensures models are used for their intended purposes, properly maintained, and subject to appropriate change controls. Effective governance requires considering these core principles: fairness, transparency, accountability, security, and safety. While laws and regulations are still emerging, it is important for insurers not to rely solely on regulatory guidelines, but to proactively develop and promote strong self-governance mechanisms that address and mitigate regulatory and business risks.
The explosion of data, combined with the limitations of traditional models and the rise of sophisticated algorithms, presents an unparalleled opportunity for insurers. Strategically applying unconstrained models can serve as a powerful catalyst for innovation.
By using methods like gap analysis, insurers can quantify the performance loss attributed to certain limitations. This insight is essential; it reveals the true cost of constraints and highlights areas where constraints may be unnecessarily hindering segmentation or value creation. Maximizing predictive power does not mean abandoning traditional methods. Instead, it means strategically enhancing them with advanced techniques and a robust framework for governance and interpretability.
This content is from work originally published by Oliver Wyman. Read the full article here.