In addition to transforming the way in which financial institutions approach predictive modeling, machine learning techniques are beginning to find their way into how model validators assess conventional, non-machine-learning predictive models. While the array of standard statistical techniques available for validating predictive models remains impressive, the advent of machine learning technology has opened new avenues of possibility for expanding the rigor and depth of insight that can be gained in the course of model validation. In this blog post, we explore how machine learning, in some circumstances, can supplement a model validator’s efforts related to:

  • Outlier detection on model estimation data
  • Clustering of data to better understand model accuracy
  • Feature selection methods to determine the appropriateness of independent variables
  • The use of machine learning algorithms for benchmarking
  • Machine learning techniques for sensitivity analysis and stress testing



Outlier Detection

Conventional model validations include, when practical, an assessment of the dataset from which the model is derived. (This is not always practical—or even possible—when it comes to proprietary, third-party vendor models.) Regardless of a model’s design and purpose, virtually every validation concerns itself with at least a cursory review of where these data are coming from, whether their source is reliable, how they are aggregated, and how they figure into the analysis.

Conventional model validation techniques sometimes overlook (or fail to look deeply enough at) the question of whether the data population used to estimate the model is problematic. Outliers—and the effect they may be having on model estimation—can be difficult to detect using conventional means. Developing descriptive statistics and identifying data points that are one, two, or three standard deviations from the mean (i.e., extreme value analysis) is a straightforward enough exercise, but this does not necessarily tell a modeler (or a model validator) which data points should be excluded.

Machine learning modelers use a variety of proximity and projection methods for filtering outliers from their training data. One proximity method employs the K-means algorithm, which groups data into clusters centered around defined “centroids,” and then identifies data points that do not appear to belong to any particular cluster. Common projection methods include multi-dimensional scaling, which allows analysts to view multi-dimensional relationships among multiple data points in just two or three dimensions. Sophisticated model validators can apply these techniques to identify dataset problems that modelers may have overlooked.


Data Clustering

The tendency of data to cluster presents another opportunity for model validators. Machine learning techniques can be applied to determine the relative compactness of individual clusters and how distinct individual clusters are from one another. Clusters that do not appear well defined and blur into one another are evidence of a potentially problematic dataset—one that may result in non-existent patterns being identified in random data. Such clustering could be the basis of any number of model validation findings.



Feature (Variable) Selection

What conventional predictive modelers typically refer to as variables are commonly referred to by machine learning modelers as features. Features and variables serve essentially the same function, but the way in which they are selected can differ. Conventional modelers tend to select variables using a combination of expert judgment and statistical techniques. Machine learning modelers tend to take a more systematic approach that includes stepwise procedures, criterion-based procedures, lasso and ridge regresssion and dimensionality reduction. These methods are designed to ensure that machine learning models achieve their objectives in the simplest way possible, using the fewest possible number of features, and avoiding redundancy. Because model validators frequently encounter black-box applications, directing applying these techniques is not always possible. In some limited circumstances, however, model validators can add to the robustness of their validations by applying machine learning feature selection methods to determine whether conventionally selected model variables resemble those selected by these more advanced means (and if not, why not).


Benchmarking Applications

Identifying and applying an appropriate benchmarking model can be challenging for model validators. Commercially available alternatives are often difficult to (cost effectively) obtain, and building challenger models from scratch can be time-consuming and problematic—particularly when all they do is replicate what the model in question is doing.

While not always feasible, building a machine learning model using the same data that was used to build a conventionally designed predictive model presents a “gold standard” benchmarking opportunity for assessing the conventionally developed model’s outputs. Where significant differences are noted, model validators can investigate the extent to which differences are driven by data/outlier omission, feature/variable selection, or other factors.


 Sensitivity Analysis and Stress Testing

The sheer quantity of high-dimensional data very large banks need to process in order to develop their stress testing models makes conventional statistical analysis both computationally expensive and problematic. (This is sometimes referred to as the “curse of dimensionality.”) Machine learning feature selection techniques, described above, are frequently useful in determining whether variables selected for stress testing models are justifiable.

Similarly, machine learning techniques can be employed to isolate, in a systematic way, those variables to which any predictive model is most and least sensitive. Model validators can use this information to quickly ascertain whether these sensitivities are appropriate. A validator, for example, may want to take a closer look at a credit model that is revealed to be more sensitive to, say, zip code, than it is to credit score, debt-to-income ratio, loan-to-value ratio, or any other individual variable or combination of variables. Machine learning techniques make it possible for a model validator to assess a model’s relative sensitivity to virtually any combination of features and make appropriate judgments.



Model validators have many tools at their disposal for assessing the conceptual soundness, theory, and reliability of conventionally developed predictive models. Machine learning is not a substitute for these, but its techniques offer a variety of ways of supplementing traditional model validation approaches and can provide validators with additional tools for ensuring that models are adequately supported by the data that underlies them.