Case Study Details

Overview

A large systemic bank holding company undertakes a comprehensive review of a network of 50+ commercial real estate credit risk models, yielding more accurate risk projection and better business decisions.

Purpose

A large bank holding company (BHC) planned to onboard a new commercial real estate (CRE) portfolio through collaboration with major life insurance companies in the US. Critical to the decision whether to onboard the new venture was an assessment of the portfolio’s underlying credit risk models. Our team was asked to perform a full scope review of more than 50 CRE credit risk models which were developed using big data techniques, covering:

  • CCAR valuation models
  • Basel PD models
  • CCAR PD models
  • Basel LGD models
  • CCAR LGD models
  • Expected Loss engine (reviewed for CCAR, Basel RWA, and Reserving purposes)

In addition to these models, the client requested that our team validate the model output adjustments defined by the business.

Approach

The McKinsey-Risk Dynamics team initiated the study by reviewing more than 700 pages of documentation within the first week to better understand the modelling environment and challenges. Digging deeper, the team relied on frequent communication with key stakeholders, model developers, and the client’s internal validation team to extract the information necessary for validation.

Using this information as a foundation, the team defined a detailed validation approach in line with the BHC’s standards. The main elements included the following:

Input Component

  • Data reconciliation checks and review of the data flow process
  • Assessment of data assumptions and treatments
  • Identification of models dependencies and related risks

Processing Component

  • Assessment of the methodological and parameter soundness
  • Review and challenge of model assumptions
  • Independent replication of the model processing components

Output Component

  • Testing of model performance, stability, and robustness
  • Construction of benchmark models

Outcome

As a result of the study, the bank’s internal validation team was able to more effectively understand the complex model architecture and underlying datasets. Multiple benchmark models containing significantly less complexity and exhibiting a better performance than the actual models, were also developed and provided to the client. Given that these models were used to substantiate the decision to on-board a new portfolio, the findings delivered were critical to the business and were implemented in later versions of the models.

Validation reports and model risk ratings were delivered in alignment with client and regulatory (e.g., SR11-7, CCAR, and Basel II) standards, and included discussions on the nature of the work performed, key validation findings, model limitations, and conditions of use.

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author-image

SAM ISBISTER

Managing Director

ABOUT US

Why Risk Dynamics?

Risk Dynamics has unparalleled experience in model risk management and independent validation of risk models. We have supported financial institutions of all sizes, worldwide, since 2004.

Our focus and dedication to model validation services, our independence and benchmarks and proprietary tools and methodologies, combined with our ability to provide unbiased views across regions and regulatory regimes, is unique in the market.