Taking the stress out of stress testing with risk model orchestration

On March 8th, a leading bank for business and cryptocurrency, along with its main regulator, announced their voluntary liquidation. In the days and months that followed, other similar banks were shut down due to insufficient liquidity and taken into receivership by the Federal Deposit Insurance Corporation (FDIC). The liquidity stress events that started in the Spring of 2023 reverberated across the banking industry and many institutions faced pressure forcing emergency liquidity actions. These events prompted many banks to wonder, “Where are my hidden risks and exposures?"  

At first glance, these failures could appear to be isolated to market risk and liquidity management. However, they illustrate the importance of effective, holistic and integrated bank balance sheet risk analytics that enable strong capital and liquidity management practices.  

Scenario and stress modeling are key in identifying and preparing for market shocks and potential gone-concern events. But as uncertainty grows, so does the need for sophistication and coordination across business planning, financial risk analytics, data modeling and stress tests. The question is, can banks unlock this information fast enough to stay ahead? 

Banks are facing many — many — challenges in complex model execution 

Economic fluctuations, geopolitical upheaval, changing regulatory rules often require dynamic models to forecast financials and manage risk.  

Many banks may have to use hundreds or even thousands of models to forecast financials, run risk measurements and stress capital and liquidity. The end-to-end process may involve various model owners, runners, diverse data sets, data platforms and analytics platforms.  

To run these models, your organization may have a mix of end-user computing solutions (EUCs), in-house developed models, vendor model solutions and deployment platforms. However, integration and executing this myriad of solutions can present significant challenges. Many execution platform vendors often require strict adoption of their built-in technology and logic, with operational penalties for customization, making it a challenge to adapt technology solutions to specific needs. This can force analysts to run one set of models within one environment while completing the analysis and reporting in another. 

While banks strive to continuously address effective model execution and operational excellence, they are also under immense pressure to establish innovative governance frameworks and audit mechanisms to enable transparency, accountability and compliance throughout the model execution process. 

As banks strive to enhance the sophistication of their models, scenario-design and forecasting, their coordination becomes increasingly important. But proliferating models may create inconsistencies among them. The challenge lies in finding a solution that can effectively connect and integrate these disparate systems and models, enabling seamless data flow and collaboration.  

To add speed, flexibility and governance to an increasingly complex — and growing number — of models, banks should address the entanglement of data modeling requirements — and the processes and technologies that support model execution. 

The solution? A human-led tech-powered approach — one that not only solves challenges from a technological perspective, but also a process and operational perspective. That’s where Model Edge, a PwC product, can come in. 

Your model management headquarters   

Model Edge’s newest orchestration capability can give banking organizations the power of model stringing — using leading open-source orchestration tools — to help register, monitor, update and run models in a more governed, flexible and automated way.

Model Edge orchestration uses an open-API framework to help push and pull information from almost any system, vendor solution or analytics platform. This can allow your organization to execute in or across environments of choice, enabling a true enterprise-wide, centralized modeling solution. With model stringing, you can connect, govern, and automate many models at one time — significantly reducing the time it takes to stress test or run capital planning analyses.  

Run reliable financial performance projections 

Banks rely on financial performance projections to confidently plan for the future. Model Edge orchestration can provide drill-down capabilities and explanation of variances between projections and actuals for valuable insights and informed decision-making. Cash forecasting helps confirm consistent cash flow management. Automated forward risk metric calculations can help your organization assess and reduce potential risks — and save time and effort. Projections and results are designed to be easily digested, making it effortless to extract meaningful information.  

Automate stress testing, scenario analysis and capital planning   

Model Edge orchestration can offer flexible, modular model execution for stress testing capabilities of potential vulnerabilities. With automated data handoffs, tracking and monitoring, those who need to know the latest information and data trail — including audit and regulatory bodies — stay aware. 

Model Edge orchestration can integrate multiple scenarios, inputs and assumptions for deeper sensitivity analyses. Model runners can explore various growth rates, interest rates, macroeconomic events and idiosyncratic factors so you can have a more holistic understanding of potential impacts on your organization. 

Analyze in-the-moment and historical metrics 

Model Edge orchestration highlights trends across key metrics and limits, allowing you to quickly identify and analyze critical patterns. Early warning indicators enable proactive risk management, allowing you to address potential issues before they escalate. Model Edge orchestration also provides detailed and easily accessible balance sheet and market data, greatly reducing the hassle of navigating multiple systems across lines of business.  

As banks face numerous challenges in complex model execution, the need for sophisticated data modeling and stress testing can become crucial. Model Edge orchestration offers a human-led tech-powered approach to help address these challenges, enhance risk management practices and make informed decisions for the future — no matter what may come.