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Beyond the code: How model risk management anchors you in reality.

As organisations increasingly rely on sophisticated algorithms to guide everything from financial investments to operational efficiencies, the stakes for accuracy, reliability, and integrity of these models escalate exponentially.

The collapse of Long-Term Capital Management (LTCM) in the late 1990s serves as a reminder of the catastrophic consequences when models go wrong. Despite being led by Nobel laureates and reputed for its cutting-edge financial strategies, LTCM's reliance on complex, highly leveraged models ultimately led to its downfall.

This is just one illustration of the need for robust model risk management (MRM) —a discipline that ensures models fulfill their intended role as navigational aids in the decision-making process. MRM is not just as a regulatory requirement or a compliance checkbox: it champions the principles of transparency, accountability, and continuous improvement. Let’s find out more:

  • The promises and problems of model risk management
  • Model risk management’s broad applicability
  • Implementing model risk management
  • Model risk management as a strategic choice

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The promises and problems of model risk management

MRM’s role can be dissected into understanding its direct impact on mitigating potential risks, building trust and transparency, optimising model performance, and, most importantly, reducing financial losses.

The journey of a model from concept to real-world application is fraught with potential pitfalls, from overfitting and data contamination to outright lack of validation. One example is the realm of credit risk modelling, where models are designed to assess the creditworthiness of loan applicants. Despite passing tests, these models can falter when exposed to the real market, incorrectly assessing risk and approving loans for high-risk borrowers, leading to defaults and financial losses. Model risk management helps identify and mitigate these risks before they escalate.

The credibility of models also rests on their ethical and compliant nature. Adhering to regulatory standards like the US Federal Reserve’s SR 11-7 is not just about legal compliance but about fostering stakeholder confidence, enhancing transparency and building trust. This is especially crucial in sensitive sectors like finance, where the integrity of models can have far-reaching consequences on customers and the broader economy.

Models are not static entities but evolve continuously through regular monitoring and feedback loops. A process of continuous validation and recalibration is essential for maintaining the accuracy and relevance of models, ensuring that they adapt to changing market conditions and emerging risks. This diligence improves decision-making – and can help secure a competitive edge by enabling more agile and informed strategies.

The most obvious benefit of robust model risk practices is their ability to prevent financial disasters. Incidents such as the JPMorgan Chase "London Whale" debacle, where flawed risk models led to a $6.2 billion loss, and Knight Capital's algorithm glitch, resulting in a rapid $440 million loss and failure of that company, serve show not only the direct financial implications but the long-term reputational damage that MRM can help prevent.

Model risk management’s broad applicability

Some people think of MRM as only important for the complex, sophisticated algorithms found in the financial sector. The truth is, from the most straightforward scoring system to the intricate machine learning algorithm, all models carry inherent risks that require diligent management.

Simple models, often taken for granted in their accuracy and reliability, are not immune to risks such as data biases, incorrect assumptions, or misinterpretation of outputs. These risks, if unaddressed, can lead to significant consequences, underscoring the need for model risk management irrespective of the model's perceived simplicity.

In healthcare, diagnostic algorithms play a crucial role in patient care, where the accuracy of a model can mean the difference between a correct diagnosis and a misdiagnosis, directly impacting patient outcomes and healthcare quality. In marketing, predictive models are used to forecast consumer behaviour, influence marketing strategies, and allocate budgets. In the utility sector, models predict energy consumption patterns to optimise grid operations and energy distribution. In agriculture, predictive models are employed to forecast crop yields, guiding farmers on planting decisions and resource allocation.

Such a variety of the potential applications of models reiterate the idea that MRM is not a niche requirement but a universal best practice. By recognising the broad applicability of MRM, organisations across all industries can leverage its principles to manage the inherent risks in their models, ensuring their operations are both effective and aligned with broader ethical standards.

Model risk management as a strategic choice

The first step in implementing MRM is to establish a robust governance framework, delineating clear roles, responsibilities, and reporting lines. Such a framework ensures model accountability and integrity by providing a clear roadmap for model oversight within the organisation, ensuring that every stage of the model's lifecycle is under scrutiny. This governance structure acts as the scaffold upon which all MRM activities are built, ensuring a standardised approach.

Robust model development practices involve a focus on data quality, bias mitigation, and model explainability. This includes the use of diverse data sets to prevent biases that could skew model outputs and comprehensive testing scenarios to ensure models are robust against a variety of conditions. Explainability is particularly crucial, ensuring models are not just black boxes but can be understood and interrogated by stakeholders, enhancing transparency and trust.

Ongoing monitoring and validation are essential to maintaining the accuracy and relevance of models over time. Techniques such as back-testing, where models are tested against historical data, and stress-testing, where models are evaluated under extreme but plausible scenarios, are critical components of this process, spotting potential model weaknesses or areas for improvement.

Effective documentation and communication are vital for transparency, both internally and for regulatory compliance. This includes documents detailing the development process, assumptions, limitations, and performance of each model. Transparent communication about model purposes, limitations, and risks to all stakeholders ensures that everyone has a clear understanding of how models are used and the potential implications of their outputs.

Conclusions and next steps for your organisation

MRM is not just a regulatory compliance requirement – it’s a strategic cornerstone for any organisation pursuing data-driven decision-making. Viewing model risk solely through the lens of compliance understates its broader strategic value and competitive advantage. Best practices in managing model risks also closely align with enterprise risk management frameworks that provide an integrated view of assessing and monitoring how risks impact an organisation’s ability to achieve its strategic objectives.

In an era where artificial intelligence and machine learning models are increasingly central to business strategies, MRM is vital to ensure these technologies are implemented responsibly and effectively, laying a foundation for AI applications that are technically sound, ethically aligned and transparent. By diligently implementing MRM practices, organisations are better equipped to navigate the complex and dynamic data landscape.

Model risk management also signifies an organisation's commitment to high standards of data stewardship, underpinning a culture that values accuracy, fairness, and accountability. This commitment not only resonates with stakeholders, including customers, investors, and regulatory bodies, but also positions the organisation favourably in an increasingly competitive and scrutinised market.

Find out more about model risk management in our insightful on-demand webinar. Jared Siddle joins our VP North America Terry Lee to look at the intricacies of Model Risk Management (MRM) and regulatory compliance. The session builds on the topics discussed in this blog to equip you with the knowledge to fortify your organisation against potential pitfalls and grow strategically:

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About the author

Jared Siddle is Protecht's Director of Risk, North America. He is a Qualified Risk Director who has been Head of Risk Management at three different companies, including two of the world's largest asset managers. Jared has proven success in banking, fund management and other financial service companies across over 26 countries. He is passionate about governance, risk, compliance and sustainability. He is an expert at designing, developing, and executing customised enterprise-wide risk frameworks.