In the wake of Silicon Valley Bank’s dramatic collapse, questions around systemic risk supervision have once again come to the fore. Mark Watson, the former head of management governance at SVB, recently cautioned that regulators failed to detect a dangerous accumulation of systemic risk before it metastasized into a crisis. His warning, delivered on the Following the Rules podcast, is not just a post-mortem of one bank’s failure but a broader indictment of supervisory blind spots in today’s complex financial landscape. ( https://followingtherules.podbean.com/e/markwatson-svb-s-formermanagement-governance-head-on-thecrisis-policymakersregulators-and-financial-institutionsaresleepwalking-into-and-howto-avoidit/)
Beyond Size: The Evolution of Systemic Risk Thinking
Since the global financial crisis of 2008, supervisory frameworks have increasingly emphasized macroprudential tools to safeguard financial stability. The Basel Committee’s guidelines for identifying Global Systemically Important Banks (G-SIBs) and Domestic Systemically Important Banks (D-SIBs) provide structured approaches to assessing institutions based on their size, interconnectedness, substitutability, and complexity. These metrics inform loss-absorbency capital buffers, recovery and resolution planning, and supervisory intensity.
While these tools are indispensable, they remain largely rooted in balance-sheet metrics and institutional presence. As banking models diversify and decentralize, size alone is no longer a sufficient proxy for systemic risk. It is becoming increasingly clear that business model fragility can amplify systemic vulnerabilities—regardless of a bank’s size or classification.
Why Business Model Vulnerability Matters
Modern banking is undergoing rapid transformation. Institutions are pursuing aggressive digitalization, platform-based strategies, and specialized niches. While these approaches may offer agility and customer reach, they also introduce new forms of risk: volatility in revenue sources, growing reliance on wholesale funding, overconcentration in high-yield sectors, or fragility from poor digital infrastructure.
Recent regulatory insights emphasize the importance of business model analysis (BMA) in identifying latent threats before they manifest in distress. A comprehensive BMA should blend quantitative metrics—such as profit trends, liquidity ratios, and funding structures—with qualitative assessments of strategic coherence, operational resilience, and execution risk.
If widely adopted, flawed or fragile business models can be effectively assessed as vectors of systemic instability.
Toward a Stronger Risk Supervision Paradigm
To address these risks, systemic risk assessments must integrate institution-level business model scrutiny with sector-wide analysis. Traditional stress testing and capital assessments need to be enhanced with forward-looking reviews of business strategy, digital readiness, and governance discipline.
Moreover, institutions should be evaluated not just on their resilience to shocks, but on the sustainability of their earnings and their reliance on key assumptions that may no longer hold true in a rapidly evolving macro-financial environment.
Crucially, supervisory assessments must include early warning indicators (EWIs) that detect declining profitability, funding fragility, or deviations from strategic plans well before regulatory thresholds are breached.
Systemic Presence can be misleading in current context
Systemic presence, measured by balance sheet size or market infrastructure role, is no longer sufficient to predict systemic fragility. Regulators must go further—embedding business model risk into systemic assessments and proactively identifying institutions whose strategies, structures, or digital dependencies could serve as shock amplifiers under stress.
To support this evolution in supervisory thinking, a practical template is presented (see Annexure) that combines traditional systemic risk indicators with a structured analysis of business model vulnerabilities. In doing so, we aim to move beyond static labels toward dynamic, holistic oversight of systemic risk in modern banking.
Annexure: Systemic Risk Assessment Template Incorporating Business Model Vulnerabilities
Systemic Vulnerability Indicators
| Category | Indicator | What to Assess | Scoring Guidance (0–5) | Score | Remarks |
| A. Traditional Systemic Risk (Max 20) | |||||
| Size | Total Assets, Market Share | Compare to sector average; impact on economy if failed | 0 = small/local, 5 = top-tier national/global | ||
| Interconnectedness | Intra-financial exposures | Reliance on other FIs for funding, trading, or operations | 0 = minimal ties, 5 = highly networked | ||
| Substitutability | Role in key financial services | Payments, custody, clearing, or niche service provision | 0 = easily replaceable, 5 = critical market role | ||
| Complexity | Use of structured assets, cross-border links | Number of legal entities, Level 3 assets, offshore activity | 0 = simple bank, 5 = opaque and hard to resolve | ||
| B. Business Model Vulnerabilities (Max 40) | |||||
| Revenue Stability | Income source quality | Stable NII/fees vs. volatile, one-off, or cyclical earnings | 0 = diversified & stable, 5 = highly volatile | ||
| Funding Fragility | Funding structure | Wholesale dependence, deposit concentration, duration mismatch | 0 = retail-stable, 5 = short-term wholesale reliance | ||
| Profitability Trends | Earnings trajectory | ROA/ROE, NIM, cost-to-income trends over 3–5 years | 0 = consistent returns, 5 = losses or instability | ||
| Strategic Execution Risk | Plan realism, delivery capability | Success/failure of past plans, internal capacity to execute transformation | 0 = aligned & realistic, 5 = overambitious/failed | ||
| Market Position Fragility | Competitive sustainability | Market share changes, customer churn, innovation lag | 0 = strong share/growth, 5 = erosion or irrelevance | ||
| Governance & Culture | Internal controls & ethics | Related-party risks, governance discipline, compliance breaches | 0 = strong & ethical, 5 = multiple red flags | ||
| Operational Resilience | IT & outsourcing risks | Tech stability, cyber readiness, vendor concentration | 0 = robust systems, 5 = repeated outages/incidents | ||
| Stress Testing Quality | Scenario design, link to plans | Comprehensive, severe, linked to recovery planning | 0 = integrated, 5 = superficial or missing | ||
| C. Early Warning Indicators (Max 40) | |||||
| Liquidity Stress Signals | Outflows, LCR/NSFR trends | Rising funding cost, drawdowns, liquidity squeeze symptoms | 0 = stable, 5 = signs of acute stress | ||
| Asset Quality Deterioration | NPLs, provisions, sectoral risk | Sectoral overexposures, rising delinquencies | 0 = low NPLs, 5 = spiking deterioration | ||
| Capital Adequacy Decline | CET1 buffer trends | Rapid drawdown or stress breach potential | 0 = strong buffer, 5 = at or below floor | ||
| Strategic Drift | Strategic uncertainty or failure | Repeated strategy shifts, unclear direction, weak delivery | 0 = coherent path, 5 = plan paralysis | ||
| Digital/Reputation Risk | Cyberattack, client trust | Complaints, data breaches, social media events, outages | 0 = no incidents, 5 = major reputational crisis |
Total Score (Max 100): ______
Composite Risk Rating Table to classify the overall systemic risk tier and guide supervisory action.
Composite Systemic Risk Rating
| Total Score | Risk Rating | Color Code | Description | Supervisory Implications |
| 0–30 | Low Systemic Risk | 🟢 Green | Strong financials and resilient business model; limited systemic exposure | Standard supervisory cycle; no additional action required |
| 31–50 | Moderate Risk | 🟡 Yellow | Emerging vulnerabilities or moderate fragility in model or indicators | Enhanced monitoring, targeted reviews, and scenario testing |
| Above 50 | High Systemic Risk | 🔴 Red | Significant risk from business model instability, capital/liquidity fragility | Corrective action plans, recovery planning updates, close supervisory engagement |
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