Stress testing in risk management often involves highly complex, computer-generated simulation models that use hypothetical scenarios as their testing framework – analysing how an organisation’s balance sheet responds to specific situations.
For example, in a period of financial uncertainty, financial organisations can deploy these models to analyse market and portfolio risk and make informed decisions based on the results. Having high-quality data at their disposal following stress tests in risk management will enable financial organisations to be more efficient, mitigate risk and identify problems early on.
The financial crisis of 2007-08 for example, represents a prime example of poor portfolio risk management. Excessive risk taking by large banks, lack of transparency, financial regulation failure and subprime lending resulted in the catastrophic collapse, leading to widespread calls for changes in financial regulation. And, as a direct result, the industry has witnessed a significant shift in bank regulation and supervision, especially regarding regulatory reporting and market risk reporting. The framework at the time, Basel II, was overhauled and reinforced with the Basel III framework. Basel III was designed to expand the regulatory scope of risk management, ensuring that financial organisations are legally required to carry out comprehensive stress testing. This enables them to manage risk and determine their ability to survive in specific financial circumstances.
From July 2018, under Basel rules, banks must produce stress tests under an IFRS9 (International Financial Reporting Standards) basis. In addition, they must also fully document how the stress test results were arrived at. Subsequently, to manage the stress testing process more effectively, mitigate risk and ensure continued regulatory compliance, financial organisations need a single, coordinated stress testing solution to manage their balance sheet.
What are the elements of Business-Driven Stress Testing?
Find out how to go beyond government-mandated stress testing to generate business-specific insights in this whitepaper.
Download the whitepaper now
What are the benefits of a stress testing solution?
With new governance-related requirements on stress testing being frequently updated, there is a clear need for a comprehensive stress testing for risk management solution. For larger financial organisations, stress testing is hardly new. What is new, however, is the level of complexity and transparency these tests require.
Current regulations and requirements for robust stress testing means that financial organisations need to be both consistent and transparent with their results. This means centralised data, data provisioning, consolidation and aggregation have become key aspects that need to be considered to meet regulatory requirements.
Problems that some financial organisations face in the pursuit of transparency and accuracy are old systems, inefficient infrastructure and trouble implementing and reporting on risk models. Old systems may result in data silos across the organisation and use inconsistent data definitions, making it difficult to consolidate that data and develop a consistent and repeatable process. Inefficient infrastructure results in disconnected processes throughout the model lifecycle, leading to inconsistencies in the stress testing results. Lastly, financial organisations may not have a well-structured, documented and transparent process that meets regulations, which means stress testing results are often erroneous and fragmented.
However, with stress testing solutions, risk management can be forensically analysed on a single, centralised platform. Financial organisations can run specific and customised financial scenarios – all of which are compliant with the latest regulations – to support ‘what-if’ assertions, assess portfolio risk, and manage capital planning.
Utilising stress testing solutions, everyone involved can work from the same, fully integrated and reconciled data source that includes consistent data definitions and a data hierarchy, ensuring that stress testing is reliable, accurate and repeatable. And, at any point in the stress testing process, the information is readily available and searchable, providing the necessary transparency needed.
Furthermore, most stress testing solutions include cost-effective model automation, enabling financial organisations to implement and execute specific risk models or various iterations of them with ease. Data can be pulled directly from existing portfolios into the system and risk models executed to aggregate them to any desired level for granular-level analysis and reporting. Through stress testing solutions, financial organisations can plan more strategically and deliver consistent financial risk reports.
Finally, with the ability to perceive risks earlier, forecasting can be carried out for portfolio development and risk mitigation to develop a company-wide view of potential impacts on capital. And, as tests can be conducted at scale, the results can quickly be translated into valuable and actionable insights that can benefit the whole organisation.
While stress testing in risk management is an absolute necessity – it needs to be viewed (and used) as more than a box-ticking exercise. Stress testing solutions are not only fundamental tools in a financial organisation’s development of its risk management strategy, but also tools that can be used to drive value and improve processes.
Rather than simply running the organisation within acceptable levels of risk, stress testing solutions need to be used for organisation-specific stress testing as well. Organisation-specific stress tests can provide end-to-end visibility to identify new opportunities and improve performance across every department.
If you want to learn more about going beyond government-mandated stress testing to generate greater value for your organisation, download the whitepaper Elements of Business-Driven Stress Testing.
- Article When it matters: Safeguarding your organization from the insideWith evolving threats, fraud detection technologies have to be flexible and nimble, and automated risk detection is a crucial component of decision advantage.
- Article Enhancing Know Your Customer (KYC) & Customer Due Diligence (CDD)IFRS 9 and CECL will require banks to more accurately predict expected credit losses (ECLs). This will require new credit loss models based on analytics.
- Article IFRS 9 impairment regulation: How to prepare for the data tsunamiBanks will have to update ECL amounts at each reporting date for credit risk changes, significantly increasing impairment calculations and data collection.
- Article Transaction monitoring: To segment or not?A typical anti-money laundering transaction monitoring program has scenarios that monitor the customers and accounts that pose the most risk. The fact is … this one-size-fits-all methodology isn’t very effective because customers transact differently based on many factors. So how do you incorporate that into your program?