The recent proliferation in machine learning models in banking and structured finance is becoming impossible to ignore. Rarely does a week pass without a client approaching us to discuss the development or validation (or both) of a model that leverages at least one machine learning technique. RiskSpan’s own model development team has also been swept up in the trend – deep learning techniques have featured prominently in developing the past several versions of our in-house residential mortgage prepayment model.  

Machine learning’s rise in popularity is attributable to multiple underlying trends: 

  1. Quantity and complexity of data. Nowadays, firms store every conceivable type of data relating to their activities and clients – and frequently supplement this with data from any number of third-party providers. The increasing dimensionality of data available to modelers makes traditional statistical variable selection more difficult. The tradeoff between a model’s complexity and the rules adapted in variable selection can be hard to balance. An advantage of ML approaches is that they can handle multi-dimensional data more efficiently. ML frameworks are good at identifying trends and patterns – without the need for human intervention. 
  2. Better learning algorithms. Because ML algorithms learn to make more accurate projections as new data is introduced to the framework (assuming there is no data bias in the new data) model features based on newly introduced data are more likely to resemble features created using model training data.  
  3. Cheap computation costsNew techniques, such as XGBoost, are designed to be memory efficient. It introduces an innovated system design that helps in reducing the computation cost. 
  4. Proliferation breeds proliferation. As the number of machine learning packages in various programming tools increases, it facilitates implementation and promotes further ML model development. 

Addressing Monitoring Challenges 

Notwithstanding these advances, machine learning models are by no means easy to build and maintain. Feature engineering and parameter tuning procedures are time consuming. And once a ML model has been put into production, monitoring activities must be implemented to detect anomalies to make sure the model works as expected (just like with any other model). According to the OCC 2011-12 supervisory guidance on the model risk management, ongoing monitoring is essential to evaluate whether changes in products, exposures, activities, clients, or market conditions necessitate adjustment, redevelopment, or replacement of the model and to verify that any extension of the model beyond its original scope is valid. While monitoring ML models resembles monitoring conventional statistical models in many respects, the following activities take on particular importance with ML model monitoring: 

  1. Review the underlying business problem. Defining the business problem is the first step in developing any ML model. This should be carefully articulated in the list of business requirements that the ML model is supposed to follow. Any shift in the underlying business problem will likely create drift in the training data and, as a result, new data coming to the model may no longer be relevant to the original business problem. The ML model becomes degraded and the new process of feature engineering and parameter tuning needs to be considered to remediate the impact. This review should be conducted whenever the underlying problem or requirements change. 
  2.  Review of data stability (model input). In the real world, even if the underlying business problem is unchanged, there might be shifts in the predicting data caused by changing borrower behaviors, changes in product offerings, or any other unexpected market drift. Any of these things could result in the ML model receiving data that it has not been trained on. Model developers should measure the data population stability between the training dataset and the predicting dataset. If there is evidence of the data having shifted, model recalibration should be considered. This assessment should be done when the model user identifies significant shift in the model’s performance or when a new testing dataset is introduced to the ML model. Where data segmentation has been used in the model development process, this assessment should be performed at the individual segment level, as well. 
  3. Review of performance metrics (model output). Performance metrics quantify how well an ML model is trained to explain the data. Performance metrics should fit the model’s type. For instance, the developer of a binary classification model could use Kolmogorov-Smirnov (KS) table, receiver operating characteristic (ROC) curve, and area under the curve (AUC) to measure the model’s overall rank order ability and its performance at different cutoffs. Any shift (upward or downward) in performance metrics between a new dataset and the training dataset should raise a flag in monitoring activity. All material shifts need to be reviewed by the model developer to determine their cause. Such assessments should be conducted on an annual basis or whenever new data is available. 

Like all models, ML models are only as good as the data they are fed. But ML models are particularly susceptible to data shifts because their processing components are less transparent. Taking these steps to ensure they are learning based on valid and consistent data are essential to managing a functional inventory of ML models.