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All case studies

Catastrophic failureFinancial Services2023

Silicon Valley Bank

Silicon Valley Bank Collapse

Estimated impact: $209B

F19

Decision Quality Index

Critical — 4 biases detected, 1 toxic combinations

SVB collapsed after a bank run triggered by unrealized losses on its long-duration bond portfolio. Management had concentrated deposits in the tech/VC sector and invested heavily in long-term treasuries and MBS without adequate interest rate hedging, anchoring to the prolonged low-rate environment.

Decision context

Whether to hedge interest rate risk on the held-to-maturity bond portfolio or maintain unhedged positions as rates began rising rapidly in 2022.

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

Groupthink★ Anchoring BiasStatus Quo BiasRecency Bias

3D Decision Knowledge Graph

Silicon Valley Bank — 4 biases, 1 toxic combination

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Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 4 detected biases

Toxic combinations

Status Quo Lock

Reference class base rates

Across all 146 curated case studies in our library:

90%

base failure rate

10%

base success rate

Lessons learned

Anchoring to a decade of near-zero interest rates caused management to treat rising rates as a temporary anomaly rather than a regime change.
Concentrated depositor bases in a single industry amplify run risk when that industry faces a downturn.
Recency bias in risk models that rely on recent low-volatility periods systematically underestimate tail risks.

Source: Federal Reserve Board Review of the Federal Reserve's Supervision and Regulation of SVB (2023) (Case Study)

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Lessons learned

Anchoring to a decade of near-zero interest rates caused management to treat rising rates as a temporary anomaly rather than a regime change.

Concentrated depositor bases in a single industry amplify run risk when that industry faces a downturn.

Recency bias in risk models that rely on recent low-volatility periods systematically underestimate tail risks.