A Framework for Model Risk Management

“There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.” – Donald Rumsfeld, 2002,  Fmr US Secy of Defense

This is the fourth in a series of blogs on Data Science that I am jointly authoring with Maleeha Qazi, (https://www.linkedin.com/in/maleehaqazi/). We have previously covered Data quality  issues @ http://www.vamsitalkstech.com/?p=5396 and the inefficiencies that result from a siloed data science process @ http://www.vamsitalkstech.com/?p=5046 . We have also discussed the ideal way Data Scientists would like their models deployed for the maximal benefit and use – as a Service @ http://www.vamsitalkstech.com/?p=5321. This fourth blogpost discusses an organizational framework for managing business risk which comes with a vast portfolios of model.  

Introduction

With machine learning increasing in popularity and adoption across industries, models are increasing in number and scope. McKinsey estimates that large enterprises have seen an increase of about 10 – 25% in their complex models which are being employed across areas as diverse as customer acquisition, risk management, insurance policy management, insurance claims processing, fraud detection and other advanced analytics. However, this increase is accompanied by a rise in model risk where incorrect model results, or design, contributes to erroneous business decisions. In this blog post, we discuss the need for model risk management (MRM) and a generic framework to achieve the same from an industry standpoint.

Model Risk Management in the Industry

The Insurance industry has extensively used predictive modeling across a range of business functions including policy pricing, risk management, customer acquisition, sales, and internal financial functions. However as predictive analytics has become increasingly important there is always a danger, or a business risk, incurred due to the judgment of the models themselves.  While the definition of a model can vary from one company to another, we would like to define a model as a representation of some real-world phenomenon based on the real-world inputs (both quantitative and qualitative) shown to it, which is generated by operating on the inputs using an algorithm to produce a business insight or decision. The model can also provide some level of explanation for the reasons it arrived at the corresponding business insight. There are many ways to create and deliver models to applications. These vary from spreadsheets to specialized packages and platforms. We have covered some of these themes from a model development perspective in a previous blog @ – http://www.vamsitalkstech.com/?p=5321.

Models confer a multitude of benefits, namely:

  1. The ability to reason across complex business scenarios spanning customer engagement, back-office operations, and risk management
  2. The ability to automate decision-making based on historical patterns across large volumes of data
  3. The audit-ability of the model which can explain to the business user how the model arrived at a certain business insight

The performance and the composition of a model depend on the intention of the designer. The reliability of the model depends primarily on access to adequate and representative data and secondly on the ability of the designer to model complex real-world scenarios and not always assume best-case scenarios.

As the financial crisis of 2008 illustrated, the failure of models brought down the insurance company AIG which caused severe disruption to the global financial system, set off the wider crisis in the global economy. Over the last few years, the growing adoption of Machine Learning models has resulted in their increased adoption into key business processes. This illustrates that if models go wrong, it can cause severe operational losses.  This should illustrate the importance of putting in place a strategic framework for managing model risk.

A Framework for Model Risk Management

The goal of Model Risk Management (MRM) is to ensure that the entire portfolio of models is governed like any other business asset. To that effect, a Model Risk Management program needs to include the following elements:

  1. Model Planning – The first step in the MRM process is to form a structure by which models created across the business are done so in a strategic and planned manner. This phase covers the ability to ensure that model objectives are well defined across the business, duplication is avoided, best practices around model development are ensured, & making sure modelers are provided the right volumes of data with high quality to create the most effective models possible. We have covered some of these themes around data quality in a previous blogpost @ http://www.vamsitalkstech.com/?p=5396    
  2. Model Validation & Calibration – As models are created for specific business functions, they must be validated for precision [1], and calibrated to reflect the correct sensitivity [4] & specificity [4] that the business would like to allow for. Every objective could have it’s own “sweet spot” (i.e. threshold) that they want to attain by using the model. For example: a company who wants to go green but realizes that not all of it’s customers have access to (or desire to use) electronic modes of communication might want to send out the minimum number of flyers that can get the message out but still keep their carbon footprint to a minimum without losing revenue by not reaching the correct set of customers. All business validation is driven by the business objectives that must be reached and how much wiggle room there is for negotiation.
  3. Model Management – Models that have made it to this stage must now be managed. Management here reflects answering questions suck: who should use what model for what purpose, how long should the models be used without re-evaluation, what is the criteria for re-evaluation, who will monitor the usage to prevent wrong usage, etc. Management also deals with logistics like where do the models reside, how are they accessed & executed, who gets to modify them versus just use them, how will they be swapped out when needed without disrupting business processes dependent on them, how should they be versioned, can multiple versions of a model be deployed simultaneously, how to detect data fluctuations that will disrupt model behavior prior to it happening, etc.
  4. Model Governance – Model Governance covers some of the most strategic aspects of Model Risk Management. The key goal of this process is to ensure that the models are being managed in conformance with industry governance and are being managed with a multistage process across their lifecycle – from Initiation to Business Value to Retirement.

Regulatory Guidance on Model Risk Management

The most authoritative guide on MRM comes from the Federal Reserve System – FRB SR 11-7/OCC Bulletin 2011-12. [3] And though it is not directly applicable to the insurance industry (it’s meant mainly for the banking industry), its framework is considered by many to contain thought leadership on this topic. The SR 11-7 framework includes documentation as part of model governance. An article in the Society of Actuaries April 2016 Issue 3 [2], details a thorough method to use for documenting a model, the process surrounding it, and why such information is necessary. In a highly regulated industry like insurance, every decision made (e.g. assumptions made, judgment calls given circumstances at the time, etc.) in the process of creating a model could be brought under scrutiny & effects the risk of the model itself. With adequate documentation you can attempt to mitigate any risks you can foresee, and have a good starting point for those that might blindside you down the road.

And Now a Warning…

Realize that even after putting MRM into place, models are still limited – they cannot cope with what Donald Rumsfeld dubbed the “unknown unknowns”. As stated in an Economist article [5]: “Almost a century ago Frank Knight highlighted the distinction between risk, which can be calibrated in probability distributions, and uncertainty, which is more elusive and cannot be so neatly captured…The models may have failed but it was their users who vested too much faith in them”. Models, by their definition, are built using probability distributions based on previous experience to predict future outcomes. If the underlying probability distribution changes radically, they can no longer attempt to predict the future – because the assumption upon which they were built no longer holds. Hence the human element must remain vigilant and not put all their eggs into the one basket of automated predictions. A human should always question if the results of a model make sense and intervene when they don’t.

Conclusion

As the saying goes – “Models do not kill markets, people do.” A model is only as good as the assumptions and algorithm choices made by the designer, as well as the quality & scope of the data fed to it. However, enterprises need to put in place an internal model risk management program that ensures that their portfolio of models are constantly updated, enriched with data, and managed as any other strategic corporate asset. And never forget, that a knowledgeable human must remain in the loop.

References

[1] Wikipedia – “Precision and Recall”
https://en.wikipedia.org/wiki/Precision_and_recall

[2] The Society of Actuaries – “The Modeling Platform” https://www.soa.org/Library/Newsletters/The-Modeling-Platform/2016/april/mp-2016-iss3-crompton.aspx

[3] The Federal Reserve – SR 11-7: Guidance on Model Risk Management
https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm

[4] Wikipedia – “Sensitivity and Specificity”

https://en.wikipedia.org/wiki/Sensitivity_and_specificity

[5] The Economist: “Economic models and the financial crisis Why they crashed too”, Jun 19th 2014 by P.W., London.

https://www.economist.com/blogs/freeexchange/2014/06/economic-models-and-financial-crisis

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