Where there is a model, there is model risk. Any company which is deploying “next-level” capabilities should ask itself, do we also have ‘next-level’ management techniques to support our new capabilities?
New technologies, market complexities, and new demands on the business are driving a heavy reliance on models. The ongoing disruption is urging organisations to invest in a wide array of new models, analytics, and data tools. By applying sophisticated modelling techniques and new tools to emerging data sets, one would be transitioning to a model-based economy which:
Where there is a model, there is model risk. This presents a unique risk for many companies as they may not have existing model risk functions, or they may have invested more in modelling capabilities than in model literacy and model risk management capabilities.
Any company which is deploying “next-level” capabilities should ask itself, “do we also have ‘next-level’ management techniques to support our new capabilities?” For context, model risk arises from actions taken and decisions made based on incorrect, misspecified, or misapplied models.
As an example, legacy models may date back years or decades, relying on tribal knowledge and declining skills to maintain a critical business tool, thus exposing companies to risk and scale problems. Other external events such as changes in reporting requirements or adoption of regulatory changes may also cause model drift or make models inadequate to support new business needs.
In the absence of adequate model governance frameworks, model management processes, and effective control structures, the residual risk may expose companies to significant losses and strategic missteps. In addition, it will be difficult to capture the benefits of significant data and analytics spend when the models don’t work.
Companies should first look to establish a robust model management framework for its combined digital and modelling efforts. This starts with a meaningful conversation to understand the current balance between modelling ambition, model risk, and model literacy. Then, companies should develop model management frameworks, operating models, and an infrastructure to help modelling ambitions scale without creating runaway model risk.
This effort should consider the six aspects of a healthy model management framework:
Effective model management can help unleash this value while mitigating risks from model error and misuse. Models do not create value by themselves— it is the organisation’s model literacy and data fluency which creates value from the models it uses to make decisions and enhance operations.
New advances in technology and available data modelling techniques promise to provide companies with significant improvements in enterprise value, customer experience, and employee experience.
We see a combination of “hard” and “soft” benefits to such an approach.
In short, model literacy helps an organisation understand, trust, and use its improved modelling capabilities, while model management helps an organisation develop a coordinated, scalable, and trusted modelling environment.