There is nothing new under the sun, as the old expression goes. But there sure are plenty of surprises. Rising interest rates, high inflation, low unemployment, supply chain concerns, elevated commodity prices, strong but evolving consumer balance sheets, low consumer sentiment, and febrile geopolitics are among factors leading to bouts of financial and economic volatility—and deepening uncertainty for bank credit exposures. Indeed, the historical data used to support credit decisions often do not compute in the current context. Many banking leaders are quickly realizing that new approaches are required to navigate current conditions and to spot potential opportunities.
Faced with an array of unusual correlations, banks need to find ways to balance macro and micro risks, incorporating the diverse factors shaping the economy and understanding the implications for clients and portfolios. However, the current combination of events is unprecedented, and the challenge cannot be finessed by simple tweaks to model parameters. To both minimize risk and unlock pockets of value, more fundamental changes are required.
As discussed in a recent McKinsey article, organizations that thrive in uncertainty hone three kinds of edge: superior insights, clarity and commitment to specific actions, and speed of execution.1 In this article, we discuss five imperatives that may support edge sharpening across the following dimensions in credit risk management:
- creating a range of scenarios to quickly model potential outcomes at a granular level
- revisiting risk limits and triggers to reflect changes in the business cycle
- creating new decisioning metrics
- prebaking menus of actions
- enabling faster and more flexible execution
Decision makers that align their credit playbooks with these five imperatives may be better equipped to navigate uncertainty and develop a deeper understanding of the factors shaping credit quality over time.
Five capabilities to navigate uncertainty
In the past year, the global economy has faced multiple challenges, and orthodoxies that have evolved over recent decades have become more uncertain. To navigate these headwinds, banks require tools to help them understand the fundamental drivers of portfolio and obligor performance. Optimally, they should also reevaluate tactical and strategic tool kits and ensure that operating models enable rapid execution. Five steps can support actions to achieve these outcomes.
1. Cultivate the ability to quickly simulate impacts on portfolios and obligors across multiple scenarios
Increased uncertainty around future events, constantly shifting drivers, and an unusual combination of economic factors require banks to run scenarios that incorporate numerous external factors. The more factors and factor combinations that they can model, the easier it will be to identify and scope potential impacts on portfolios and obligors.
To support accurate modeling, scenarios must go beyond traditional approaches, many of which rely on a few standardized macroeconomic inputs. In a period of increased complexity, scenario generation requires more granular factors, incorporating both economic and broader uncertainties (for example, geopolitical risks, supply chain shocks). These should be combined with agile forecasting capabilities that enable rapid calculation of potential portfolio income and losses. Leading institutions enable faster action by generating new metrics every two weeks or every month, rather than every quarter, as was common in the past.
To develop insights on the portfolio and obligor level based on scenarios, some banks are embracing new approaches to forward-looking credit assessment (Exhibit 1). To that end, they are exposing a range of transaction metrics to discrete combinations of granular macroeconomic drivers—for example, food prices and utility bill inflation or rent increases and retail-customer interest charges. This approach enables banks to identify microsegments that may be vulnerable to specific scenarios or may prove more resilient. While the vulnerable microsegments present risks, the more resilient segments create potential opportunities for sustainable growth.
To effectively conduct these analyses, many banks are turning to highly automated implementation platforms that are capable of modeling and refining multiple scenarios and enabling analysis of impacts across portfolios and segments (macroeconomic or driver based). In many cases, the platforms incorporate a business-driver forecasting module, focusing on variables including scenario-conditioned volumes, revenues, and expenses.
2. Refine risk limits and triggers
At most banks, current levels of risk appetite were set during an extended period of low interest rates and dampened volatility. Current economic consensus suggests these conditions may not return anytime soon. Indeed, the reasonable assumption is that the business cycle has shifted, and through-the-cycle portfolio behavior may significantly change. Banks therefore need to revisit through-the-cycle views of client performance in a higher rate environment, as well as verify that monitoring frameworks, triggers, and cascading mechanisms are still relevant and workable—from both a risk management and business growth perspective.
In assessing risk limits, it makes sense to proceed by business unit, product, industry, and geography. Limits for measures—including “one in X year” losses, the impact of stress scenarios, and the portfolio effects of downgrades or defaults—should take into account shifting correlations and potential idiosyncratic events. This will lead to limit reanchoring that better reflects potential risks and outputs under different scenarios, as well as generating new estimates of capital needs.
Banks should also consider baseline- and stress-loss outcomes, using the information to reevaluate triggers around risk appetite. There will be areas in which they want to tighten up on credit provision, but others where the risk/return trade-off may be more favorable in the next two to three years, based on the assumption that through-the-cycle portfolio behavior will be different than in the past.
At one bank with a diversified corporate portfolio, this exercise generated surprising results. Projected scenarios showed that the bank’s diversified portfolio had become relatively more concentrated in smaller sectors of the economy. This prompted decision makers to reevaluate sector concentration limits and refine individual obligor limits to better match the expected risk/return profile.
3. Develop forward-looking decisioning metrics
Effective analysis is predicated on having access to appropriate metrics, but current metrics are often backward looking; their ability to predict the future is tightly bound to relationships with historical trends. In a volatile world, in which many of those historical relationships are being upended, the predictive power of existing approaches is limited. In response, banks need to develop more forward-looking metrics that highlight risks and opportunities quickly enough to formulate a sensible strategy.
Creating a longer horizon of predictability is no simple task, but it can help to break performance down into groups of significant drivers and assess relevant trends both at portfolio and obligor levels. One institution built a performance matrix, plotting a range of business drivers (for example, a drop in demand, risks and receivables repayments, or dependency on energy) against potential impact intensities across industries (Exhibit 2). It periodically reviewed the trends around drivers, helping calibrate the outlook for each industry under evolving scenarios. This was helpful in both managing risk and identifying pockets of opportunity.
For large individual obligors, it can make sense to go further, modeling revenues and costs under various scenarios and shocks to create cash flow curves and understand debt service coverage. This process can both highlight red flags and point to growth opportunities. Some banks are adding continuous-monitoring tools. These generate early-warning signals based on financial and forward-looking KPIs such as news flows (Exhibit 3), and can indicate declining credit quality as much as 12 months in advance. Forward-looking indicators can also help risk managers define triggers for timely action at portfolio and obligor levels.
4. Use decision metric outputs to inform and ‘prebaked’ management levers
Accelerating change implies a higher bar for management preparedness. To adapt to deepening uncertainty, leadership teams can benefit from developing a set of “prebaked” actions that can be implemented at short notice. Aligning in advance also allows for more creativity than decisions made at the spur of the moment, and will enable more clinical execution when required.
Through judicious monitoring of forward-looking metrics and indicators, bank leadership teams can take effective action across diverse aspects of credit oversight, from designing collections/repossessions to adjusting portfolio allocation and refining customer engagement strategies—as well as timely planning for second-order impacts such as talent shortages. To ensure effective implementation, training at scale and across functions may be required; for example, upskilling of relationship managers and credit analysts on restructurings.
5. Enable agile decision making through cross-functional perspectives and flatter hierarchies
Typical decision-making hierarchies are often insufficiently nimble to respond to a highly unpredictable environment. Banks need to rework their governance frameworks to enable much greater speed of decision making. While it can help to prebake actions and define initiation parameters, mobilization is also a challenge. To be effective, decisions should be operationalized through existing governance processes but at much faster speeds.
Speedy decision making requires efforts to ensure that at each forum there is 360-degree information flow, facilitated by cross-functional collaboration. In addition, there needs to be much more real-time interaction between the risk function and the front office. The objective should not be absolute precision, but rather an increased ability to rapidly understand the direction of travel so that actions can be aligned. Similarly, to minimize bottlenecks, authority should be delegated within prescribed limits. Evaluating and adjusting authorities based on plausible scenarios will ensure they remain fit for purpose and help the organization react more rapidly.
The route to transformation
As the global economy continues to surprise and the interest rate environment resets, banks should assess whether they have the capabilities and processes in place to create the three edges that will help them manage through uncertainty.
A good place to start is a structured evaluation of capabilities and processes, potentially through analysis across a single representative portfolio. This can help decision makers rapidly identify the capabilities that need to be enhanced across the board. Similarly, focusing on a few select, high-impact portfolios can help illuminate pockets of value. Optimally, the exercise should be undertaken both from a risk function and business perspective, helping ensure that risks are managed and value realized across the institution.
A range of digital tools can provide additional support. For example, machine learning can help identify and classify deposits and card-account spending in different categories. This can be aggregated at the borrower level to determine likely disposable income and potential shocks under various scenarios. Finally, a continuous-monitoring tool can centralize data from treasury transactions, news, forward-looking industry-specific indicators, and markets to generate segment- and obligor-level early-warning signals.
Amid persistent complexity, bank leadership teams need to urgently revisit their approaches to credit risk management. To navigate the changes required, there is an impetus to take action across the five dimensions discussed above, with analysis and responses optimized through highly automated implementation platforms.
As decision makers consider their options, a helpful first step will be to revisit current capabilities and resources and enhance data and forecasting capabilities—as well as to reconsider the assumptions that underlie them. At the very least, this will require refreshed tool libraries and more agile decision-making frameworks. Effectively implemented, these will help banks hone edges in credit insight, clarity, and execution, and help them marshal the inevitable risks and opportunities that define a new era of uncertainty.
