The 2008 VaR Model Replay#

History Doesn’t Repeat Itself, But It Often Rhymes.

The year is 2008. The global financial system is on the brink of collapse. The cause? A complex web of factors, to be sure, but at the heart of the crisis was a simple and seductive idea: that risk could be quantified, that it could be reduced to a single number, a “Value at Risk” or “VaR.”

The VaR model was a triumph of mathematical elegance and of regulatory convenience. It was a tool that promised to make the financial system safer, more efficient, and more profitable. And for a time, it seemed to work.

But the VaR model was built on a foundation of flawed assumptions. It was a model that was designed for a world of normal distributions, of gentle curves and predictable outcomes. It was a model that was blind to the reality of “fat tails” and “black swans,” of sudden and catastrophic events that defy all prediction.

When the crisis hit, the VaR model failed. And it failed spectacularly. The very tool that was supposed to manage risk had become the primary source of it.

The Mechanism of Systemic Synchronicity#

Today, we are making the same mistake again—but at a scale and velocity that makes 2008 look like a trial run.

The Financial Stability Board (FSB) and other global regulatory bodies have issued critical warnings regarding a phenomenon termed “algorithmic herding” or “outcome homogenization.” This risk mirrors the structural causes of the 2008 financial crisis but operates at a velocity and scale that renders traditional circuit breakers ineffective.

In 2008, the crisis was exacerbated because major financial institutions utilized identical Value-at-Risk (VaR) models. When market conditions shifted, these models simultaneously signaled “sell,” triggering a liquidity spiral that collapsed the system. Today, the homogenization of AI foundation models creates an identical dynamic—but the cascade happens in milliseconds, not weeks.

Current Homogenization Metrics:

• Credit Decisioning: Approximately 87% of banks are deploying or testing one of three major foundation model families (GPT-4/5, Claude, Gemini variants) for assistance in credit and risk workflows
• Trading Algorithms: Over 90% of algorithmic trading strategies increasingly rely on transformer-based architectures trained on overlapping historical datasets
• Data Overlap: It is estimated that 60%+ of the training data for financial AI models overlaps across institutions, leading to correlated biases and blind spots

When models are trained on the same history, they hallucinate the same future.

The diversity of the AI ecosystem is an illusion. Under the hood, the vast majority of these systems are powered by the same handful of foundation models from the same handful of companies. They are:

• Trained on the same datasets (Common Crawl, financial news corpuses, economic indicators)
• Built on the same architecture (Transformer variants)
• Optimized with the same techniques (RLHF, supervised fine-tuning)
• Aligned with the same regulatory frameworks (Basel III, Dodd-Frank)

The result: A new and more dangerous form of algorithmic monoculture. The models are more sophisticated, the data more granular, but the underlying logic is converging. When one model sees risk, they all see risk. When one tightens credit, they all tighten credit. Synchronously.

The FSB’s October 2025 report explicitly highlights this “herding behavior” as a primary source of new systemic risk, noting that model opacity prevents supervisors from identifying these correlations until they manifest as a crisis.

The Millisecond Cascade#

Marcus, a Risk Analytics Lead at a major global bank, describes the danger with chilling precision:

“People compare this to 2008, and they’re right to be terrified. But 2008 was a slow-motion disaster. VaR models took days or weeks to fully cascade. AI models will do the same thing in milliseconds.”

Consider a hypothetical stress event in May 2027:

Trigger: A geopolitical shock triggers volatility in energy markets. Perhaps a critical undersea cable is severed, or a major oil facility goes offline unexpectedly.

T+0ms: Market data hits the trading clusters. News APIs parse headlines. Sentiment analysis algorithms flag “high uncertainty.”

T+50ms: Homogenized AI trading agents across all major firms, analyzing the same news feeds with similar “sentiment analysis” weights, simultaneously decide to dump liquidity in emerging market debt. Thousands of sell orders hit the order book in the same 10-millisecond window.

T+100ms: Simultaneously, credit risk models at major banks, sensing the volatility via API feeds, automatically tighten lending criteria by 40-60 basis points across the board. Small businesses see credit lines frozen. Trade finance disappears. Letters of credit become unavailable.

T+200ms: Supply chain AIs, reacting to the credit tightening and energy price signals, cancel inventory orders globally to preserve cash. Just-in-time logistics networks begin unwinding. Container bookings are cancelled. Warehouse orders are put on hold.

T+300ms: Liquidity providers, seeing the coordinated selling, widen spreads dramatically. Market depth evaporates. Circuit breakers trigger in equity markets, but credit markets have no such protections.

T+500ms: The first human traders notice unusual market behavior. By the time they can comprehend what’s happening—let alone convene a risk committee or call a regulator—the cascade is complete.

The result is a “Flash Crash” not just of stock prices, but of the real economy—credit, logistics, and liquidity—before a human regulator can even convene an emergency meeting.

Traditional financial circuit breakers are designed for human-speed panics. They pause trading for 5 minutes, 15 minutes, or an hour—time for humans to assess and decide. But these mechanisms are useless when the decision cycle operates at sub-second speeds and spans not just trading but credit allocation, supply chain logistics, and infrastructure management.

The FSB’s October 2025 report explicitly highlights this “herding behavior” as a primary source of new systemic risk, noting that model opacity prevents supervisors from identifying these correlations until they manifest as a crisis. By then, it’s too late. The models are black boxes. The correlations are emergent properties of training data overlap. There is no “off switch” that can be pulled without shutting down the entire financial system.

Outcome Homogenization: When “Personalized” Means “Identical”#

The most insidious aspect of AI homogenization is not just systemic risk—it’s outcome homogenization at the individual level.

Research demonstrates that even when institutions use “custom” models, if those models share the same architecture (e.g., the Transformer) and pre-training data (Common Crawl, standard financial corpuses), they produce identical outcomes for specific individuals or groups.

The “Universal Rejection” Effect:

If Bank A’s AI denies a loan to a specific applicant based on obscure correlations in their data footprint—perhaps a pattern in their transaction history, their social media activity, or their geographic location—Bank B’s AI, trained on similar patterns, is statistically nearly certain to do the same.

This creates a “universal rejection” effect where an individual or business is not just denied by one vendor, but algorithmically exiled from the entire financial system simultaneously.

There is no second opinion in a homogenized system.

In theory, there could be a “Rashomon set” of equally accurate models that arrive at different conclusions—model diversity without sacrificing performance. But in practice, efficiency pressures and regulatory compliance demands drive all vendors toward the same optimal (and biased) weights.

Concrete example:

A small farmer in Iowa applies for an equipment loan. Bank A’s model flags them as “high risk” because:

• Their geographic region has high climate volatility (drought risk)
• Their business model doesn’t include expensive carbon tracking software
• Their ESG score is “undefined” (too small to be rated by third-party services)

The farmer tries Bank B, Bank C, and a fintech lender. All deny the application with minimal explanation. Why? Because all four institutions use foundation models trained on the same ESG compliance corpuses, the same climate risk datasets, and the same historical default data.

The farmer is not high risk. The farmer is algorithmically unbankable.

This is not hypothetical discrimination—it is emergent discrimination, arising from the complex interaction of training data, model architecture, and regulatory frameworks. And because it is emergent rather than explicit, it is nearly impossible to challenge or remedy under current legal frameworks.

Civil rights law, including “disparate impact” doctrine, requires proving that a specific policy or rule caused discrimination. But when the discrimination emerges from a billion-parameter neural network’s feature interactions, there is no “policy” to challenge. The model simply learned that certain patterns correlate with risk—patterns that happen to perfectly align with protected class membership or geographic/economic marginalization.

The victim of this process has no recourse. They are trapped in a Kafkaesque loop where:

• The decision-maker is a non-human entity that cannot explain its reasoning
• The bank claims it is simply following “data-driven risk management protocols”
• The model vendor disavows responsibility for specific outcomes
• The regulator lacks the technical tooling to audit a billion-parameter neural network in real-time
• The homogenization means there is no alternative vendor to turn to

This creates a class of “algorithmically unbankable” entities—individuals, small businesses, entire communities—permanently excluded from the formal economy not by explicit policy, but by the invisible hand of correlated machine learning.

Sources: [1] Midhafin, “The Role of VaR in the 2008 Financial Crisis”, https://www.midhafin.com/the-role-of-var-in-the-2008-financial-crisis [2] Quora, “What was the role of VaR in the 2008 financial crisis?”, https://www.quora.com/What-was-the-role-of-VaR-in-the-2008-financial-crisis