Stress testing is not new to the risk world but has been a major focus since the GFC (Global Financial Crisis). For a number of years now, stress testing has helped analytical specialists quantify various aspects of potential loss. What is new is the introduction of regulatory stress tests which has added to the work load that banks face. Banks are also faced with increasing complexity, increasing frequency and the firm-wide nature of regulatory and economic scenarios that are required to be tested. So stress testing programs are placing increasing demands across an organisation’s people, processes and IT systems – particularly banks and now our insurance companies in Australia. This change in scope has introduced several challenges. On the technology side, the challenges are mainly due to the scale and complexity of the underlying data required to build out the scenarios and stress tests. This is stretching the capabilities of existing computing resources to deliver timely responses.

Our banking and insurance customers are very focused on stress testing. In fact,  I engaged with a number of Australian customers this year to explore stress testing automation soon after teams spent weeks of late nights and weekends completing the first round of APRA’s annual stress test.

Our discussions uncovered a common set of challenges across institutions:

• Limited stress-testing framework: Stress testing has historically been an isolated exercise by a bank’s risk function, different stress events and assumptions are used in each model and outputs cannot be easily aggregated into a meaningful, combined result.
• Lack of granularity: Today’s systems are often incapable of providing the granularity needed in each asset class at the individual position or facility level.
• Insufficient coverage: Disparate data in large quantities prohibits a consolidated view of all assets. This is further exasperated when trying to bring risk and finance data together.
• Inconsistency: Multiple versions of data, valuation methods and models persist, making it practically impossible to achieve consistency among calculations and measures.
• Organisational silos: While many trading desks and risk groups conduct stress tests to supplement their risk analyses, they are done in silos by individual analysts, making it impossible to verify that assumptions are consistent.
• Coordination across functions: Consolidating and analysing the interdependencies across strategy, risk, budgeting and treasury activities adds even greater complexity.

Based on recent work with customers, leading consulting companies and auditors, SAS identified the following key focus areas required to deliver a successful stress testing program:

• Efficiency: Integration of existing risk models and data hierarchies into a streamlined data infrastructure for firm-wide stress testing. Data, computations, reporting –all must be part of a unified platform.
• Performance: The efficient aggregation of results for all major risk models across the organisation with the ability to run complex, forward looking stress tests with multiple parameters.
• Enterprise View: Views of economic capital and pro forma financials at the enterprise level with a view of market, credit and liquidity risk. Leading practice is to extend this to include iterative rounds of planning in terms of anticipated business growth and treasury funding activities.
• Transparency: The ability to understand and document model assumptions, design and structure so they are readily apparent to management and regulators with the elimination of the “Black Box” characteristic of some models.
• Compliance: The capability to address major regulatory stress testing issues as they evolve with the ability to integrate them into the risk decision making process.

To explore some of the advances in technology that can help you meet evolving requirements, listen to the on-demand webinar “Stress testing: A fresh point of view.” Industry experts discuss how banks are using analytics to:

• Run full cycle stress tests in days instead of weeks;
• Create consistent and repeatable processes; and
• Handle numerous valuation methods, disparate data and stress testing models.