There are many models designed to help build organisational resilience and manage change. From Kotter’s Unfreeze, Change, Freeze or his Eight-Step Process, to the Transition Curve, there are many different ways to understand and visualize change. One of the simplest, but extremely powerful, is the People, Processes, Technology (PPT) framework.
The idea behind this framework is simple: the three elements of people, processes, and technology need to work in harmony for successful change. This is particularly helpful when the change you are considering involves technology. Working for a software company, I know that it can be tempting for organizations to decide that simply buying a piece of software will solve all their problems. The PPT framework makes it clear that this is not the case.
As modeling and analytics have become more complicated—especially with the advent of artificial intelligence (AI)—this framework has become even more important. Financial institutions such as banks and building societies now build and use a huge number of models. Managing model risk is, therefore, a vital part of their operations, and often requires expert help.
People, Processes, and Technology in Banking
Viki Styrbæk is a Director at Deloitte in Denmark, where she works with model risk management (MRM) together with banks and insurance companies. I asked her about her experience with balancing the three elements of the framework.
“You cannot deliver change without taking people with you. Banks that have fully implemented model risk management functions need the whole organization to be on board with that approach. Stakeholders have to take responsibility for managing their own model risk. So, the people aspect is essential. However, in many of the conversations we have with banks, we see that many of their model risk management processes don’t necessarily work. Other departments in the bank see these processes as excessive or not value-generating.”
Viki points out that technology is essential for improving processes, perhaps through automation. She believes that technology and processes support each other. Her conclusion is simple:
“If you took away both processes and technology, but people in the organization understand how to develop, use, and maintain models correctly, model risk would still be mitigated. On balance, therefore, I think the most important aspect of model risk management is people. But technology and processes are also needed to support change and to implement and maintain effective and transparent model risk management.”
In particular, Viki suggests that understanding is vital.
“Model risk is relatively new, and therefore we need to make sure that people who work with models understand how they work and their own role in this connection. The risk is significant, and it will only grow over time as the use of models increases. We want models to make decisions, especially the easy ones, to support faster business processes. This will free up time for people to focus on more difficult tasks. That means people must understand and manage the risk of the models, just as they understand and make more complex decisions.”
“It’s not only technical. We also need to see a change in governance and systems. It comes back to people, processes, and technology. They all need to be aligned.”
The long view on model risk management
Effective management of model risk in financial institutions hinges on the harmonious integration of people, processes, and technology. As the complexity of analytics and the use of artificial intelligence continues to rise, it is clear that relying on technology alone is insufficient to mitigate risks.
While technology and processes are crucial for enhancing efficiency and transparency, it is the people—those who develop, use, and maintain the models—who play the most critical role. Ensuring that everyone within an organization understands how models function and their responsibilities in managing associated risks is key to navigating the increasing challenges posed by model risk.
As businesses continue to evolve and adopt AI-driven models for decision-making, the emphasis on people becomes even more vital. Educating employees on model risk and promoting an organizational culture that prioritizes responsible management are essential steps in safeguarding against potential pitfalls. In tandem, the processes and technology should be optimized to support this understanding, creating an environment where model risk is actively mitigated.
Ultimately, the alignment of people, processes, and technology ensures that model risk management not only becomes more robust but also drives business innovation and resilience. Organizations that successfully balance these elements will be better equipped to manage the ever-expanding landscape of model use, ensuring both operational efficiency and risk mitigation.
If you wish to learn more about how analytics is making more possible if we can get the people equation right, my colleagues have discussed options in this GARP article.
