In today’s interconnected financial environment, institutions operate with sophisticated operational risk databases and state-of-the-art cyber defenses. Yet the real challenge lies not merely in detecting threats, but in understanding the long-term consequences of seemingly isolated incidents. Operational disruptions, governance failures, or cyberattacks rarely exist in a vacuum; their impact is often shaped—and amplified—by the broader macroeconomic and market context.
For example, cyber incidents surged during the COVID-19 pandemic, not just because of heightened digital activity, but also because organizations operated under unprecedented stress. Human error rates rose in parallel, as employees adapted to remote work, changing procedures, and heightened workloads. This interplay between stress, operational vulnerability, and macro shocks reveals that the same event can have radically different outcomes depending on the surrounding environment.
An operational mishap that might be quickly contained in a stable economy could escalate dramatically during a recession. Under such conditions, even a minor lapse—if amplified by targeted social media campaigns—can trigger a reputational crisis whose costs dwarf the original operational loss. Earnings may fall, liquidity may tighten, funding costs may rise, and regulatory scrutiny may intensify. What begins as a micro-level incident can cascade into a multi-dimensional crisis.
To truly grasp how an IT failure, cyberattack, or governance gap can ripple through an institution, risk managers must embrace a holistic, integrated strategic risk analysis. This means connecting initial shocks to their long-term consequences on capital adequacy, credit losses, liquidity buffers, and earnings stability.
The Case for Forward-Looking, Synthetic Scenarios
Traditional stress testing has limitations. By relying on a small set of predetermined scenarios, institutions may overlook the vast space of potential futures. To anticipate unprecedented dynamics, institutions must consider thousands of possible futures, not just a handful.
This is where synthetic data becomes invaluable. Synthetic data—artificially generated rather than collected from actual events—expands the scope of risk analysis. Unlike historical datasets, which reflect only one realized history, synthetic datasets offer a multitude of possible market evolutions. They enable the modeling of both first-order impacts (direct losses) and higher-order effects (changes in behavior, liquidity flows, regulatory responses).
Many standard risk models – whether Monte Carlo simulations or econometric regressions – suffer from a critical flaw: they replicate past statistical relationships but will not produce genuine surprises. They ignore extreme outliers – those rare but drastic shifts that often do the most damage. But markets are non-stationary: correlations shift, volatilities spike, and behavioral patterns evolve.
The solution is to separate stable market behavior from shock periods and sample from each independently, while introducing unprecedented shocks into the mix. By doing so, forward-looking scenarios can blend historical realism with imaginative risk exploration.
Generative models – trained to identify the “signatures” of both smooth and abrupt market movements – can create synthetic datasets that preserve historical credibility while embedding new and unexpected events. Even when historical training data lacks major booms or busts, the resulting synthetic scenarios can still model disruptive shocks.
A Three-Step Framework for Scenario-Driven Resilience
Institutions can apply this approach through a structured three-step process:
- Link Core Business Metrics to Macro Variables
Use explainable machine learning techniques (e.g., regression with regularization) to connect loan volumes, unfunded commitments, deposits, and all pre-provision net revenue (PPNR) components to macroeconomic and market drivers. - Generate Comprehensive Scenarios
Create thousands of scenarios—including historical events, extreme market shocks, and newly hypothesized disruptions—and calculate their knock-on effects. This approach naturally produces shifts in historical correlations, revealing hidden vulnerabilities. - Simulate Strategic Outcomes
Apply the dependency relationships from Step 1 to the generated scenarios, producing integrated projections for asset-liability management (loan and deposit levels with associated interest rates), credit books (default, prepayment, and collateral valuations), and key performance indicators such as capital ratios, liquidity buffers, and earnings trajectories.
This process enables utilization of synthetic data for creating a decision-making tool that tests strategic plans against a far wider – and more realistic – range of conditions.
The purpose of synthetic scenarios is not to predict the future, but to map the full landscape of plausible outcomes. By embedding these forward-looking simulations into governance and strategic planning, institutions can:
- Stress-test against both known and unprecedented events.
- Anticipate behavioral shifts in deposits, lending, and trading activity.
- Identify inflection points where targeted intervention can prevent escalation.
- Strengthen regulatory compliance through proactive, institution-specific scenario planning.
Reverse stress testing – working backward from adverse end-states to the conditions that caused them – can pinpoint the exact decision junctures where management action would be most effective.
This process is critical for digital transformation. Most institutions have already completed the first stage of digital transformation: digitization, or converting records into digital form. The second stage – digitalization – remains incomplete. This stage is about using data, not just storing it. It requires integrating advanced analytics into enterprise-wide decision making.
Synthetic scenarios represent the missing link. They allow decision-makers to move beyond static reports and toward dynamic, interactive risk management that adapts in near-real-time. In an environment of policy volatility, inflationary pressures, and interconnected operational-financial risks, this agility is essential.
Conclusion
Fragmented, siloed approaches to risk management no longer suffice. Operational disruptions and cyber incidents, when filtered through unstable macro conditions, can escalate into existential threats. The only effective countermeasure is a holistic, scenario-driven framework – one that considers all feasible combinations of shocks and their consequences, uses synthetic data to explore thousands of possible futures, and embeds forward-looking simulations into everyday governance.
By integrating cascading risk analysis, early warning signals, and synthetic scenario modeling, institutions can prepare not just for the next crisis they expect, but for the one they cannot yet imagine.
Alla Gil is co-founder and CEO of Straterix, which provides unique scenario tools for strategic planning and risk management. Prior to forming Straterix, Alla was the global head of Strategic Advisory at Goldman Sachs, Citigroup, and Nomura, where she advised financial institutions and corporations on stress testing, economic capital, ALM, long-term risk projections and optimal capital allocation.