Beyond static stress testing: Real-time risk management for an unpredictable world

Risk is inherent to capital markets, but implementing dynamic risk assessment gives banks the visibility they need to stay competitive in turbulent times.

Unpredictability is becoming all too routine for banks.

Their risk management efforts regularly face off against factors such as economic uncertainty and unforeseen events like regional conflicts and natural disasters.

Moreover, leadership teams often feel overwhelmed by a new set of emerging risks that range from cryptocurrency exposure and digital asset volatility to climate change and cybersecurity threats.

But a bigger problem for banks is that traditional static methods for stress testing fall far short in today’s turbulent environments. The methods are typically reactive and time-consuming, and they lack the ability to evaluate events’ risk impact in real time.

Risk will always be part of capital markets, but by converting static stress tests to dynamic risk assessment, banks can proactively identify risks and potential impact in near real-time.

Here are the top five shortcomings of static stress testing, as well as guidance for how automation and new risk management tools create processes ready for turbulent times.

1.    Traditional stress testing in risk management is heavily reactive.

Most stress testing processes are largely backward looking. The models rely heavily on historical data and economic indicators, making it difficult to assess newer, less understood risks. For instance, many stress tests don't adequately model how climate policy changes could affect long-term asset values, or how crypto market crashes could impact traditional banking operations.

How banks can adapt: New technologies enable proactive testing. For example, by embedding early warning systems, banks can identify risk build-up based on real-time events. The early warning gives banks time to initiate measures to safeguard themselves and prevent losses.

2.    Lead times run long, and support processes are manual.

Traditional processes rely on a strict sequence of data collection, event assessment, scenario creation, and stress reporting. This workflow is largely manual and typically stretches four to six weeks. The result is potentially costly delays in risk-related insights and decision-making.

How banks can adapt: Dynamic scenario generation automates stress testing—and shortens the process dramatically. It also provides the advantage of being powered by market research and knowledge of previous events. By automating event summary reporting, for example, dynamic scenario generation enables banks to significantly streamline stress testing. Dynamic scenario generation is just one example of the benefits of an artificial intelligence-powered stress framework. AI-driven frameworks can automate a range of tasks, from risk identification and scenario generation to stress reporting, allowing risk managers to focus on proactive risk mitigation rather than repetitive manual tasks.

3.    Infrastructure support is insufficient.

Calculating and predicting intensive risk measures requires a robust infrastructure that’s ready to scale. But many banks still operate legacy, on-premises risk systems that are not designed to handle real-time market volatility. The outdated systems result in computational inefficiencies and hinder banks’ ability to analyze risks at scale. Their lack of flexibility makes it difficult to integrate real-time data and advanced analytics for efficient stress testing.

How banks can adapt: Digital and cloud technologies can help financial institutions handle increasing velocities and varieties of data for on-demand stress testing and everyday business functions. When it comes to stress testing, modern infrastructures provide several advantages. First, they enable banks to process large-scale data efficiently with scalable computing resources. They also can perform parallel stress tests on multiple risk groups such as equity, interest rates, and credit risk. Importantly, they significantly reduce operational costs. For example, our AI-driven stress testing model, hosted on a cloud-based infrastructure, enables clients to reduce costs by up to 60%.

4.    Siloed data makes it hard to identify risks across business units.

Traditional stress testing frameworks often rely on fragmented risk models across business units, leading to blind spots in risk exposure. Without cross-functional data integration, risk teams struggle to detect interdependencies among market, credit, and operational risks.

How banks can adapt: AI/machine learning (ML) solutions identify patterns and structures, allowing banks to anticipate potential risks and vulnerability across business lines. For example, an AI-powered system can detect a correlation between corporate deposits and withdrawals and increasing foreign exchange volatility, signaling potential liquidity risk. This detection allows banks to take preemptive measures, mitigating financial disruptions before they escalate. ML models increase efficiency and accuracy and reduce the overall time required for stress testing, allowing banks to make intelligent and data-driven decisions.

5.    Stress testing costs run high.

Current stress testing is a long and expensive process. It involves infrastructure and myriad data requirements, and it also requires model development and maintenance with skilled professionals. As noted, identification of events and market research are often conducted manually. Every process involves costs and time, with system reruns, scenario generation and exposure validation adding up to huge operational costs.

How banks can adapt: Dynamic stress tests have the potential to reduce operational costs by up to 75%. In addition to cost savings, AI/ML frees risk managers from manual tasks such as conducting research and empowers them with detailed event information and insights into why events occurred. What’s more, it enables them to create and ingest scenarios on the fly. Equally important, it establishes a holistic, forward-looking compliance environment by accelerating run times and increasing efficiency.

While risk is inherent to capital markets, dynamic risk assessment gives banks the visibility they need to stay competitive in turbulent times.