Banking models after COVID-19: Taking model-risk management to the next level The COVID-19 pandemic has revealed unexpected flaws in the business models that banks rely upon. How can they best address this challenge? The COVID-19 pandemic is taking a terrible toll in human life and in the livelihoods of millions the world over. As people and institutions struggle to contain the spread of the virus, the measures necessarily imposed have caused major economic disruptions. Every industry has been affected, and banking is no exception. Capital, profit-and-loss, and liquidity positions have been hit very hard. One consequence has been that banks' models have broken down across their business. The flaws have put the reliability of these models in doubt and suggest that they cannot be trusted to help banks navigate through the crisis. Few business leaders could have foreseen a global economic shutdown of this magnitude. The models that financial institutions depend on to run their businesses simply did not account for such a crisis. Most models are almost by necessity designed to predict a stable future. In truth, the real failure is not that banks used models which failed in this crisis but rather that they did not have fallback plans to manage when the crisis did come. There are a number of reasons for the failures. First, model assumptions and boundaries defined at the design stage were developed in a pre-COVID-19 world. Second, most models draw on historical data, without the access to high-frequency data that would enable recalibration. Finally, while access to the needed alternative data is theoretically possible, models would not be able to integrate the new information in an agile manner, because the systems and infrastructure on which they are built lack the necessary flexibility. Banks are experiencing ever more model failures, and further issues can be expected with time. Financial institutions must now urgently review their model strategies. They need to develop and apply both efficient short-term actions and a long-term plan to improve model resilience. Over two prioritized time horizons, banks can carry out coordinated model adjustments to enable business continuity in the short term while reviewing their model development and redevelopment needs and upgrading their model-risk-management (MRM) frameworks over the longer term. COVID-19 has affected model reliability across all bank functions and operations Model issues are not confined to one business or function but instead have emerged in every aspect of a bank's operations. The effect on standard operations is widespread: Rating models are inaccurate because they are unable to update scores rapidly, rendering them irrelevant in assessing creditworthiness across sectors or customer segments. Early-warning-system (EWS) indicators are showing a misleading number of signals, causing a loss of predictive power. Liquidity models are failing to predict large outflows and portfolio rebalancing, thereby putting liquidity positions at risk. Model-based market-risk approaches are overreacting to stressed price and credit, as well as to liquidity shortages, leading to inflated profit-and-loss impact and costly extra funding of cleared and over-the-counter (OTC) transactions.

Phase one: Moving to a crisis-operating mode for model-risk management In the first phase, banks focus on effectively adjusting models to make them fit for purpose and mitigate the risks of poor business decisions. The adjustments should be made quickly but also efficiently and consistently to avoid undue redevelopment or readjustment costs. We recommend that a dedicated taskforce be created to lead banks in crisis-operating mode. To run the MRM crisis response effectively in a highly disruptive situation, the team should have clear governance, a disciplined operating model, and useful MRM tools. It will lead a rebalancing effort away from business-as-usual activities to crisis activities. Taking an agile approach, the team should perform a quick and effective MRM review. Using clear methodologies and its MRM tools, including model inventory and an MRM crisis-response dashboard, it can then develop and implement a well-organized crisis-response plan. We recommend that this consist of four parts: 1. Inventory of model adjustments and models at risk. The inventory will identify models that have failed or are likely to fail in the near future. It should then identify all model adjustments that have been applied and map them against the identified models at risk. 2. Consistent model-mitigation actions. Model adjustments should be applied consistently across functions and operations. The MRM team should ensure cross-checking of model adjustments and underlying assumptions for the different types of models to ensure consistency and to prevent contradictory messages and decisions. 3. Timely review of model adjustments. The team should quickly perform an effective challenge of all model adjustments and underlying assumptions planned, taking an agile approach and applying a focused review methodology. 4. Short- and long-term redevelopment plans. Model adjustments and model-redevelopment needs must be prioritized according to the criticality of the model for the business and probability of failure. Once this is complete, banks must review the applied model adjustments and the recommended model-redevelopment or model-adjustment needs. Phase two: Moving to the next level of the model-risk-management journey Banks need to use MRM in a more strategic and fundamental role, as banks move proactively to manage their portfolios of models. The purpose of MRM will be to enhance business efficiency and management decision making while increasing the resilience of the model landscape. To enhance their MRM, banks should develop solid framework elements to inform business and strategic decisions. While MRM will add value in a number of ways in the current situation and the overall model strategy, the following core elements can be considered essential: Overview of models at risk and model contagion. Banks should be able to identify models at risk by evaluating whether and how each model is essential to business and banking operations. It should enhance tiering and model-risk-assessment methodologies to gauge exposure to failuremodel limitations and boundaries. The overview should also enable the evaluation of model interdependencies. This capability will allow banks to assess and anticipate the risk and impact of model contagion. Model contingency plan. The bank should review model-risk-appetite statements and enhance model boundaries and limitations with clear tolerance levels for specific scenarios. A fallback solution should be developed for models at risk with zero or low tolerance for failure (high

criticality). A plan is needed for the continuity of model-related activities in case of disruption. The plan might include a remote operating model, remote access to data systems, and adequate infrastructure to continue activities. Dynamic MRM dashboard. The dashboard is an MRM tool that can be configured to alert the bank of emerging models at risk. Plans for business-wide model redevelopment and MRM enhancement can be integrated into the tool, which can also enable the tracking of progress against milestones based on key performance indicators. Flexible and versatile talent pool. Banks need people with the necessary expertise and capabilities to identify the models at risk across different functions and businesses and to perform focused model-risk assessments. The team should work under clear program governance, ensuring visibility and accountability of business-critical activities, such as model development, adjustments, review, and monitoring. Source: Laurent, M., Plantefve, O., Tejada, M., Weyenbergh, FV. (2020) Banking models after COVID-19: Taking model-risk management to the next level [online]. Available from: https://www.mckinsey.com/business-functions/risk-and-resilience/our-insights/banking-models-after-covid-19-taking-model-risk-management-to-the-next-level [Accessed on 02 April 2022]

QUESTION 1 (20 Marks) The case study presents real life impacts on the banking sector and the implications of how risk models can fail. 1.1 Using an appropriate diagram summarise the building blocks of a risk model and briefly highlight the risk modelling process. (11 marks)

1.2 Discuss the reasons why the risk models failed? (9 marks)

QUESTION 2 (20 Marks)

The case study further argues that the reaction to fix the risk models triggered new risks.

2.1 Summarise the causes that triggered those new risks and their respective reasons why they were triggered? (8 marks)

2.2 Identify at least four (4) risks that were triggered from the bank's reaction and their respective causes? (12 marks)