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Are Credit Risk Managers equipped to navigate the troubled waters on the horizon?

The short answer is yes and no. Over the past decade, primarily driven by regulation and to some degree competition, banks made significant improvements in credit risk processes, models, and systems. As a result of these investments, the banking industry is undoubtably better equipped to navigate the next crisis. We’ve already seen how improved credit risk management tools were used to safely negotiate the market shocks resulting from the pandemic. However, these capabilities are yet to be tested in a true downturn period.

As we’ve set out in the first blog in this series, the credit cycle is turning, and credit risk levels are rising. The latest data suggests that inflation is receding, but wages have not kept up with inflation. Bureaus are also reporting a rise in the demand for credit, reinforcing the point that consumers are being squeezed. The Bank of England’s latest Credit Conditions Survey, highlighted that:

  • The availability of credit has generally decreased; and
  • Default rates generally increased and are expected to further increase in Q1.

Looking beyond regulation and proactively managing the risk

Our 2023 Financial Markets Regulatory Outlook highlighted that UK lenders are facing a significant risk of increased impairments. With the bleak economic backdrop, firms need to go beyond the minimum regulatory expectation in their management of credit risk. At a minimum, banks will need to understand their portfolios and determine vulnerable customer segments as well as high-risk sectors (e.g., real-estate, housing, energy and leveraged exposures to name a few). In consumer portfolios, some supervisors are even going as far as expecting deeper analysis of high-risk employment sectors (e.g., aviation) with additional Stage 2 triggers or overlays to account for the risk.

Supervisors already expect lenders to use appropriate early warning indicators (EWIs) to identify emerging risk to help identify potential signs of distress in anticipation of credit risk events. These indicators may also be used to support the IFRS 9 stage classification framework. Proactive risk identification, staging, overlays, and provisioning practices to appropriately differentiate clients of varying riskiness will help weather the storm.

Traditionally, we’ve had to rely on historical relationships to support these risk management practices. Relationships that are now breaking down due to the current levels of economic, geopolitical and climate uncertainty. With old risk indicators weakened, we believe more can be done to leverage data and analytics to complement current credit risk practices.

This is where firms should make the most of advances in the field of analytics, supplementing their internal insight, to support proactive monitoring and insight driven portfolio segmentation. Such practices will deliver better data and management information, that in turn support proactive management of emerging risks.

There is a need to identify and manage risks as they emerge

As a new downturn unfolds, it is fundamental to identify emerging risks and think about the response strategy, early. It is important to consider multiple potential futures rather than placing the bet on a single outcome informed by previous experience. This requires setting early warning indicators against the range of potential risks. Essentially widening the field of vision when looking into the storm, but also remembering that things don’t necessarily (or most likely won’t) unfold as expected.

Supervisors already expect us to incorporate forward-looking indicators across a range of scenarios in our credit risk models and risk management frameworks. The purpose is to remove the undue reliance on historical perspectives. Essentially, historical data won’t yet capture the current levels of uncertainty or economic and behavioural changes that we’ve experienced in recent years. This brings the relevance of historical data into question. Simply put, most lenders are overly reliant on infrequently updated traditional data sources (e.g., current account information, credit card data, pre arrears data, central credit register data, or year-end financials) to support risk management processes.

We believe that credit risk managers should go further, exploring the use of real-time or near real-time data (essentially data that is processed and delivered quickly enough to be useful for decision making). This will help improve the timing of risk identification, allowing earlier intervention. We are seeing some firms go even further, augmenting these traditional data sources with alternative data sources, such as, news or social media data in their early warning frameworks.

The message is simple, if you want to improve your anticipation capabilities and create competitive advantage, then stop relying primarily on lagging information in reporting data.

Gaining a competitive advantage as the tide turns

As the credit cycle turns, we are moving into a dynamic situation where economic conditions can change rapidly and unpredictably. To respond to such a situation, a robust early warning framework or system becomes a critical anticipation capability for identifying emerging risks. As risks are identified, portfolio managers can start thinking about the mitigating actions that support the customer and protect the bank against losses.

The concept of Early Warning is simple, we want to identify a series of potential risks across all dimensions and then set early warning indicators against those risks. Building an automated early warning system (EWS), however, is difficult as this requires adoption of real-time and often alternative data sources. These data sources introduce two challenges:

  1. The sheer volume of data can make it difficult to prioritise alerts that are coming in; and
  2. Alternative data sources are mostly unstructured (e.g., news data) introducing subjectivity.

Fortunately, as the field of analytics has evolved there are now tried and tested techniques available to overcome these challenges. Once such a system is in place, the focus shifts to monitor the trend over time, as we’ll be able to see the risk evolve. As highlighted in one of our earlier articles, credit deterioration can be identified months in advance of a default event in some alternative data sources.

For consumer portfolios there is benefit in analysing the Income and Expenditure of customers in collections, comparing this to the underwriting assessment and bureau score. By stratifying this population, lenders would get a better understanding as to where the real pain points lie and how accurate underwriting assessments are.

Finally, impact assessments, using for example dynamic what-if analysis, can be used to help manage the risk as it evolves. Naturally, the EWS can also be used to identify opportunities in sectors less affected by the downturn, helping inform portfolio composition as the tide turns.

Helping our customers

Increasingly, banks are being expected to take on more of a social role to support customers and society. Financial pressures introduced by the cost-of-living crisis will force many individuals to seek access to credit and banks will need to have a firm grasp of affordability and risk. This is where the capabilities that we’ve described can also help banks support customers. By understanding vulnerable segments, lenders can appropriately price for the risk and proactively set up additional support schemes, such as, budgeting tools, financial education programs, and debt consolidation options.

As the Dutch philosopher, Desiderius Erasmus said: “prevention is better than cure”. However, to prevent we need to have the right tools and techniques to anticipate and respond before it is too late.

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