Model Risk Management: Strengthening Model Governance

This paper explores the key reasons why financial firms establish MRM frameworks and describes the transition from ad hoc individual model analysis to formal firmwide model governance and validation functions.

Model risk management (MRM) is the process of identifying, assessing, monitoring and controlling the business risks associated with using mathematical models. Model risk arises when business decisions are reliant on complicated models for purposes such as derivative valuation, market risk assessment, credit scoring and fraud identification.

A typical catalyst to formalise an MRM policy is either an external requirement (such as regulatory compliance) or when management faces reduced insight and input into model selection and usage controls (for example, as an asset management start-up grows to multiple managers and asset classes).

Establishing model risk management begins with formalising model governance, tracking models and their usages in a model inventory. This typically includes methodology documentation such as the purpose of the model, its inputs, outputs, assumptions and limitations, as well as meta-information about governance – development history, validation results and ongoing monitoring activities. Models are quantified for materiality and their methodologies are assessed against mitigating measures. Best practice is for suitability assessments to be undertaken by an individual or body that is independent from the model development and usage. An MRM policy then details control processes, such as what restrictions should be placed on problematic models and timescales for remedial action to be taken.


  • When to establish an MRM policy
  • Is a separate model validation function necessary?
  • Model risk pitfalls
  • Protect profitability and reputation
  • Third party support for MRM

Request A Copy


Navigate major trends & developments shaping the industry


Managing Inflation Risk with Hedging Strategies

Inflation can erode the value of investments and reduce the purchasing power of cash flows, which can be particularly detrimental to fixed-income investments.


How Data Science is Transforming Finance

The past decade has seen the rise of emerging technologies across the financial industry and beyond. There are now synergistic groups of technologies that are operating at scale and will further accelerate digitisation, boost resilience and drive operational efficiencies.


Managing Equity Volatility

In 2022, after several years of largely benign macro-economic conditions, aided by the soothing drip feed of government stimulus, equity markets woke up to a new reality. As investors gazed in horror at vertiginous descents in value, there was a rebirth of interest in listed and OTC instruments with payouts indexed to volatility.

Let's Talk!

Speak with one of our solution experts