Counterparty Insight Bank Risk Rating Methodology

Overview

The Risk Rating model reports each FDIC-insured depository institution's condition as a 1–100 strength score, where 100 is strongest. The strength score is a direct, continuous, monotonic re-expression of the model's underlying CAMELS composite, itself a continuous quantity, so institutions are distinguished across the full 1–100 range. This document describes that internal engine. The strength score is a proprietary risk indicator, not a credit rating; Counterparty Insight does not impose a pass/fail cutoff. Each institution sets the score it considers acceptable for its own counterparties. The model is based on the CAMELS framework used by U.S. federal banking regulators, adapted for quantitative analysis using publicly available regulatory financial data.

The model evaluates six components across 17 financial ratios, applies component-level weights, and produces a single composite rating. Each bank is scored against its asset-size peer group using frozen percentile thresholds pooled from 20 quarters of historical data (~94,000 institution-quarter records across ~4,500 institutions). This pooled calibration eliminates score instability caused by quarter-to-quarter threshold drift.

Rating Scale

Counterparty Insight reports a continuous 1–100 strength score (100 = strongest). The underlying composite is itself continuous (a weighted average of seventeen ratios across six CAMELS components, smoothed across quarters), and the strength score is a strictly monotonic transform of it. The score varies continuously, so two otherwise similar institutions still receive distinct scores.

Qualitative Adjustment Principles

Beyond the quantitative CAMELS scorecard, established rating methodologies recognize several qualitative factors that influence bank creditworthiness. While most cannot be systematically measured from public data, they inform the model's qualitative adjustments and hard floors:

• Business and Geographic Diversification: Banks deriving revenue from multiple distinct business lines and geographic markets exhibit greater resilience to sector-specific shocks. Conversely, concentrated or monoline business models face elevated vulnerability. The model's revenue diversification ratio partially captures this, and the small bank adjustment reflects the limited diversification of smaller institutions.

• Complexity and Opacity: Organizational and legal complexity increases management challenges and heightens the risk of strategic errors. Large, complex banking groups with numerous business lines across many geographies require ongoing adaptation to various regulatory frameworks, additional risk management, and significant funding needs. The model's G-SIB designation partially captures the systemic complexity of the largest institutions.

• Strategy, Risk Appetite, and Governance: Overly aggressive business strategies and risk appetites that exceed peer norms are often principal causes of bank failure. Rapid asset growth, aggressive dividend policies, and weak risk management are leading indicators. The model captures these through the asset growth ratio and efficiency metrics within the Management component, as well as the Texas Ratio and Loans-to-Deposits hard floors.

CAMELS Components

The "Baseline Weight" shown in each component heading below is the analyst-set prior, the starting weight used before machine-learned refinement. After ML retraining the prior is blended 50/50 with the model's data-driven weight and a 12% minimum floor is applied. See the Composite Rating section for the current effective weights.

C -- Capital Adequacy (Baseline Weight: 15%)

Capital adequacy measures the institution's ability to absorb losses and support growth while maintaining sufficient capital buffers above regulatory minimums. The component captures both the reported regulatory capital cushion and the hidden capital erosion sitting in the held-to-maturity (HTM) securities portfolio, which is carried at amortized cost on the balance sheet and not reflected in book equity.

A -- Asset Quality (Baseline Weight: 30%)

Asset quality is the most heavily weighted component because credit risk is the dominant risk for most banks. These ratios measure the quality of the loan portfolio and the adequacy of loss reserves.

M -- Management (Baseline Weight: 10%)

The regulatory CAMELS Management component is inherently qualitative (board governance, compliance, audit, strategic planning). Since these factors are not available from public data, this component uses quantitative proxy metrics that reflect management effectiveness through operational efficiency, growth discipline, revenue strategy, and reserve prudence.

E -- Earnings (Baseline Weight: 20%)

Earnings quality and sustainability determine the institution's ability to build capital organically, absorb losses, and fund growth without excessive leverage.

Why TTM ROAA. Reported quarterly net income is annualized and noisy: seasonal revenue patterns, one-time gains/losses, and provisioning lumpiness can move single-quarter ROAA materially even when the underlying franchise is unchanged. The TTM computation smooths across four consecutive quarters of profitability so the Earnings score reflects sustained capital generation rather than quarter-to-quarter noise. Banks lacking the cross-quarter history needed for TTM (newly chartered institutions, recent reporting-format conversions) fall back to single-quarter annualized ROAA.

L -- Liquidity (Baseline Weight: 15%)

Liquidity ratios assess whether the institution can meet its obligations under both normal and stressed conditions without incurring unacceptable losses.

S -- Sensitivity to Market Risk (Baseline Weight: 10%)

Sensitivity measures the institution's exposure to adverse movements in interest rates, foreign exchange, equity prices, and commodity prices. For community banks, interest rate risk and CRE concentration are the dominant concerns.

Scoring Algorithm

Step 1: Ratio-Level Scoring

Each ratio is scored on a continuous 1–16 scale by interpolating the ratio's value within its 15 frozen percentile threshold boundaries (see Threshold Calibration). The thresholds themselves are unchanged; what changed is that the value is no longer snapped into one of 16 integer buckets. Instead the model locates the two adjacent thresholds that bracket the value and returns a fractional score reflecting where the value sits between them. Two banks whose ratios differ only slightly receive slightly different scores, even within the same percentile band, so no resolution is lost to rounding.

For "higher is better" ratios (e.g., ROAA, Equity/Assets), thresholds are in descending order:

Threshold[0] > Threshold[1] > ... > Threshold[14]

value >= Threshold[0]                     → score 1   (top performers)
Threshold[i] > value >= Threshold[i+1]    → score interpolated continuously
                                            between (i+1) and (i+2), by the
                                            value's position within the band
value <  Threshold[14]                     → score 16  (worst performers)

For "lower is better" ratios (e.g., Noncurrent Loans, Efficiency Ratio), thresholds are in ascending order:

Threshold[0] < Threshold[1] < ... < Threshold[14]

value <  Threshold[0]                      → score 1   (top performers)
Threshold[i] <= value < Threshold[i+1]     → score interpolated continuously
                                            between (i+1) and (i+2), by the
                                            value's position within the band
value >= Threshold[14]                      → score 16  (worst performers)

A value falling exactly on a threshold scores the integer at that boundary; any value between two thresholds takes the continuously interpolated fraction.

For "U-shaped" ratios (e.g., Asset Growth Rate), the raw value is first transformed to a deviation from the optimal midpoint before scoring with the standard lower-is-better logic. For Asset Growth, the optimal midpoint is 5%, so the scored value is |rawGrowth - 5|. A bank growing at exactly 5% has a deviation of 0 (best possible score), while banks at -5% and +15% both have a deviation of 10 (equally penalized). The deviation-based thresholds are computed from the same peer-group percentile distribution as other lower-is-better ratios.

If a ratio value is null, undefined, or NaN (e.g., the institution does not report that field), the ratio is excluded from the component calculation.

Step 2: Component-Level Scoring

Each CAMELS component score is the simple average of its constituent ratio scores, excluding any ratios that returned null and any ratios whose raw value is below their contribution floor:

Component Score = Sum(eligible ratio scores) / Count(eligible ratio scores)

A ratio is eligible when:
  - Its scored value is non-null, AND
  - If it defines a minimum-value floor, the raw value meets or exceeds it

The contribution floor is used selectively for ratios where a benign reading (typically zero) would mechanically pull the component average toward the best possible score for every bank, an unintended side effect of simple averaging. The floor is currently applied to HTM Unrealized Loss / Tier 1 in the Capital component (5% floor), so banks with no HTM losses score Capital purely from the Leverage Ratio, while banks with material HTM losses average the two. The ratio is still shown in the per-bank breakdown when below the floor, with an annotation that it is not counted toward the average, so the rationale is transparent in both the UI and exported credit reviews.

If all ratios in a component are null or below their floors, the component score is null and excluded from the composite.

Step 3: Composite Rating

The composite score is a weighted average of the six component scores, with weights renormalized if any component is null:

Composite = Sum(Component Score_i x Weight_i) / Sum(Weight_i)

Where the analyst-set baseline weights (the prior) and the effective ML-refined weights currently deployed are:

¹ Effective weights are ML-refined and retrained weekly; the values shown are approximate and current as deployed (see the detailed breakdown below for exact current values). Large banks are not reweighted: G-SIBs and public-market banks receive fixed qualitative adjustments (−2 / −1 on the composite; see Qualitative Adjustments), not a different weight mix.

Weight rationale: The prior places the highest weight on Asset Quality because credit risk (the probability and severity of borrower default) is the primary driver of bank failures. The ML refinement then adjusts the prior toward the components most predictive of distress in the recent training window, currently trimming Asset Quality and raising Liquidity and Sensitivity (the 2023 rate-shock cohort), with a 12% floor so no CAMELS dimension is neglected. Capital is the loss-absorption pillar; Earnings reflects internal capital generation; and Management uses quantitative proxy metrics for governance quality.

Machine-Learned Weight Refinement (Shrinkage to Analyst Prior)

The baseline weights above are the prior; the model refines them weekly using machine learning trained on historical bank distress outcomes. The optimizer fits a logistic regression on the six CAMELS component scores to predict whether a bank will become distressed within the next four quarters, where "distressed" is defined as any of: actual failure (FDIC failed-bank list), Texas Ratio crossing 50% in a future quarter, composite score crossing 13, or a raw quarterly composite jump of ≥3 points.

Why use an analyst prior at all? Four reasons:

1. The distress label is partially tautological with one component. The label includes "Texas Ratio crosses 50% in a future quarter," which is itself an Asset Quality signal. A pure ML fit learns "Asset Quality predicts Asset Quality deterioration" and stacks weight on A; the prior breaks that circularity so the other CAMELS dimensions retain meaningful influence.
2. Cycle stability. The training window is only ~4 years. Whichever crisis dominated that window dominates the raw ML weights, the 2008–2014 credit-crisis era yields high Asset Quality weight; the 2021–2025 rate-shock era yields high Sensitivity and Liquidity weight. Without a prior, the framework would whipsaw every retrain depending on which kind of cycle was in the rear-view mirror. The prior anchors to a through-the-cycle view so the weights do not chase the last crisis.
3. Statistical thinness on positives. Bank failures and distress events are rare relative to the universe of bank-quarters. A logistic regression on six features over a few thousand positive events is noisy; the prior acts as a Bayesian regularizer that pulls the estimate toward a credible default when the data is too sparse to override it confidently.
4. Model-risk defensibility. For SR 11-7 model risk management review, "the analyst CAMELS framework is the starting point and the model refines it from data" is materially easier to defend than "the optimizer decided to put X% on one component." Reviewers expect to see structural reasoning behind weight allocations; the prior is that structural reasoning, and the ML refinement is the data overlay on top of it.

Shrinkage to the prior. An unrestricted machine-learned fit can drift heavily toward whichever components were most predictive in the recent training window, which is rarely what an experienced credit analyst would weight through-the-cycle. Earlier vintages of the model over-weighted Asset Quality for the tautology reason above; the current training window (2021–2025) concentrates raw weight in Sensitivity and Liquidity instead, reflecting the 2023 rate-shock episode (SVB, Signature, First Republic) where AOCI exposure and deposit-flight dynamics were the dominant failure mode. Either pattern risks under-weighting components whose effect on distress is genuine but indirect, or whose predictive lift collapsed temporarily during the training window. To balance what the data says against the structural framework, the final weights are a 50/50 blend of the ML output and the analyst prior:

Final Weight = 0.5 × ML weight + 0.5 × Analyst Prior weight

Both vectors sum to 1, so the blend preserves the simplex. The ML half re-ranks toward what the recent record favors while the prior half retains the through-the-cycle framework.

Minimum-weight floor. A 12% floor is then applied so every CAMELS dimension carries weight that materially affects the composite. Components below the floor are raised to 12% and the deficit is redistributed proportionally from above-floor components, keeping the total at 100%. The floor exists because ML can drive a component close to zero during cycles where its predictive lift temporarily collapses; the floor ensures the framework remains diversified and that no CAMELS dimension can be silently neglected.

Effective weights (model version 3, retrained 2026-06-21 on 16 quarters / 74,843 records / 3,266 distress events):

Two components (Capital and Management) currently sit at the 12% floor; their unfloored blend values are roughly 10.7% and 11.8% respectively. Sensitivity and Liquidity carry above-prior weight because the recent training window includes the 2023 rate-shock cohort. The trained model is versioned and stored in S3 with the full calibration parameters (shrinkage alpha, minimum-weight floor, analyst prior, raw ML weights) embedded in the saved object for auditability. The model retrains weekly so the effective weights drift as the underlying distress relationships evolve.

Threshold Calibration

Frozen Pooled Percentile Thresholds (Primary)

The primary scoring method uses frozen peer group percentile thresholds computed from 20 quarters of pooled historical data (~94,000 institution-quarter records). Each bank is scored against institutions of similar asset size, eliminating the systematic bias that occurs when applying a single set of absolute thresholds across banks of vastly different size and business model.

Why pooled (frozen) thresholds: In earlier versions, percentile thresholds were recomputed each quarter from that single quarter's population. This caused score instability in the trend view: a bank's composite score could shift across quarters even if its fundamentals were unchanged, purely because the peer distribution shifted (e.g., industry-wide NIM compression would move the NIM threshold, penalizing all banks equally). By pooling 20 quarters of data into a single threshold set, the boundaries are stable and any quarter-to-quarter score changes reflect actual changes in a bank's financial condition.

Why peer scoring is necessary: Large GSIBs (e.g., JPMorgan, Bank of America) naturally operate with lower capital ratios (~7% leverage vs ~10%+ for community banks), lower net interest margins (~2.5% vs ~3.5%), and higher efficiency costs due to their global operations and regulatory complexity. Absolute thresholds would penalize these structural differences as weaknesses, even though they are normal for the peer group.

Peer Groups

Each bank is assigned to its peer group based on total assets from its most recent quarterly filing.

Bell-Curve Score Distribution

The percentile thresholds use a right-skewed bell-curve distribution, so the median bank in each peer group scores near the center of the distribution:

The right-skewed shape reflects the empirical reality that most FDIC-insured institutions are fundamentally sound, while a smaller tail exhibits material weaknesses. Most of the distribution's mass therefore sits in the upper scores, with a long thin tail toward the weakest scores; the median bank scores near 78. The asymmetry reduces reliance on large qualitative adjustments by allowing the quantitative scorecard itself to produce strong scores for sound institutions.

For each ratio direction (lower-is-better or higher-is-better), a set of 15 percentile points is computed from the pooled peer-group distribution and used as the boundaries between adjacent grades. Score 1 corresponds to the best ~1% of the peer-group distribution; Score 16 corresponds to the worst ~1.5%. The percentile spacing is denser near the median (where most banks cluster and small ratio movements should not shift grades) and sparser in the tails (where any extreme reading is meaningful).

Minimum Data Requirement

Ratios with fewer than 10 valid data points in a peer group are excluded from the percentile computation for that group. This primarily affects Peer Group 1 (>$100B), which has ~30 institutions per quarter (~600 institution-quarter records when pooled). If a ratio has insufficient data in the bank's peer group, the system falls back to all-institutions thresholds (see 3-Tier Fallback below).

Threshold Refresh Cadence

The pooled threshold set is refreshed weekly on an automated schedule. Each refresh pulls the latest 20 quarters of institution financials, enriches them with cross-quarter computations (TTM ROAA, year-over-year asset growth), pools the records into a single dataset, and computes the percentile boundaries for each of the 17 ratios across each peer group plus the all-institutions population. All previously scored quarters are then re-scored against the refreshed thresholds so historical scores remain comparable to the current quarter.

If the pre-computed threshold store is unavailable, the system falls back to single-quarter live computation (see Frozen vs. Per-Quarter Thresholds below).

Frozen vs. Per-Quarter Thresholds

3-Tier Threshold Fallback

Threshold lookup for each ratio uses a cascading fallback:

1. Peer group thresholds, the bank's asset-size peer group percentiles (primary path).
2. All-institutions thresholds, the full FDIC population percentiles, used when the bank's peer group does not have enough data points for a given ratio (the minimum-data requirement above).
3. Static thresholds, a last-resort calibrated set used only when the percentile-threshold service is unavailable.

The threshold source actually used for a given bank is exposed in the UI so reviewers can see which path produced the score.

Static Fallback Thresholds

The last-resort static threshold set is calibrated using two methods:

• S&P Rating Calibration for ratios where mid-market and large-bank S&P ratings provide a usable regression: median ratio values for 22 S&P-rated U.S. banks are regressed against the bank's S&P issuer credit rating mapped to a target CAMELS-style score, and the regression line is sampled at the 15 grade-boundary points.
• Expert Grid Interpolation for ratios where the S&P regression produced inverted slopes (a consequence of business-model heterogeneity among custody banks, G-SIBs, and traditional lenders). These thresholds are informed estimates based on regulatory PCA capital thresholds, FDIC Quarterly Banking Profile industry medians, Federal Reserve supervisory guidance (e.g., SR 07-1 for CRE concentrations), and standard banking industry benchmarks, interpolated from an 8-point expert grid to the 16-point scale.

Important: Static thresholds are absolute (not peer-relative) and will systematically penalize large banks. They exist only as a degraded-mode fallback and are not used when the peer-group or all-institutions threshold service is reachable.

Data Sources

All input data is sourced from public regulatory filings made by FDIC-insured depository institutions. No customer or proprietary data is used in scoring. New data typically becomes available approximately 60 to 90 days after the end of each calendar quarter.

The model uses the 20 most recent quarters of available financial data for threshold calibration and trend scoring:

• All 20 quarters are pooled to compute the frozen percentile thresholds.
• Each of the 20 quarters is scored individually against those frozen thresholds, so a bank's full history is comparable to its current quarter.
• The 4 most recent quarters are surfaced as the trend/history view on the bank profile.

Qualitative Adjustments

After computing the raw quantitative CAMELS composite score, the model applies qualitative adjustments that shift the composite score by points. These reflect structural advantages and disadvantages that peer-group-relative scoring cannot capture.

These are hard floors, not additive adjustments: each one overrides the weighted-average composite when applicable, and the highest applicable floor wins.

The final composite score is calculated as:

Adjusted Score = clamp(Raw Quantitative Score + Qualitative Adjustments, 1, 16)

Hard floors (highest applicable floor wins):
  Loans/Deposits > 115%   → floor at 11
  Loans/Deposits > 130%   → floor at 12
  Loans/Deposits > 150%   → floor at 14
  Texas Ratio > 40%       → floor at 12
  Texas Ratio > 60%       → floor at 14
  Texas Ratio > 100%      → floor at 16
  TTM ROAA < -1%          → floor at 13
  TTM ROAA < -3%          → floor at 15

Final Score = max(Adjusted Score, applicable floor)

where the score is bounded to the [1, 16] rating scale. The hard floors are applied after all additive adjustments, ensuring that no combination of favorable adjustments (e.g., G-SIB support) can override an asset-quality, earnings, or liquidity constraint that's already in distress territory.

Floor stickiness across blending: Floors are evaluated against the current quarter's ratios, but the published composite is a 3-quarter weighted moving average (50% current, 30% prior, 20% two-quarters-ago). To preserve the "highest applicable floor wins" rule across the blending step, the floor is re-applied after blending: if the bank's current-quarter floor is more severe than the blended composite, the composite is set to the floor's boundary value. This prevents floor signals from being diluted by prior quarters where the floor wasn't triggered (e.g., a bank whose Loans/Deposits ratio jumped from 95% to 105% this quarter immediately reflects the loans-to-deposits floor rather than blending back toward its two prior in-band quarters).

Why these specific floors: The CAMELS composite is a weighted average across six components, and each component is itself a simple average across multiple ratios. That double-averaging dampens single-ratio extremes: a 62% Texas Ratio only pins 2 of 4 Asset Quality ratios at score 16, and even a maxed-out Earnings component (one ratio at weight ~11%) can only shift the composite by a point or so. For most banks this is the right behavior because credit risk is multi-dimensional, but for banks already in distress on a specific dimension, the composite under-rates the credit risk a creditor would actually face. The hard floors recover the right behavior at the tail without distorting the middle of the distribution.

Qualitative Context (Display Only)

The model also displays additional qualitative context that is not incorporated into the numerical rating but provides useful interpretive context:

• Asset Size: Total assets, asset tier classification (Mega/Large/Mid-Size/Community/Small), total equity
• Liquidity Context: Total deposits, deposits-to-assets ratio, loans-to-deposits ratio
• Market Position: Number of offices/branches, headquarters location, deposits per office

Model Validation

The rating is backtested against the historical record of bank failures to confirm that lower grades correspond to genuinely higher failure risk. Two questions are tested separately: does the rating rank-order failures (discrimination), and do the failure rates implied by each grade match what actually happened (calibration). A third test checks that ratings are stable quarter to quarter rather than jumping around.

Discrimination

Discrimination is measured with the Accuracy Ratio (Gini) derived from the cumulative accuracy profile: banks are ranked from worst grade to best, and the curve plots the share of eventual failures captured against the share of the population reviewed. A Gini of 0 is random ordering; 1.0 is perfect ordering. Two tests are run:

Out-of-sample (2008 crisis). The strictest test. Thresholds are calibrated using only pre-crisis quarters (2005–2007), the only information the model could have had at year-end 2007, then frozen and used to score the 2008–2010 crisis window (~89,000 institution-quarters, 363 failing banks). The model could not have seen the crisis when it was calibrated.

Full history. The same scoring run measured across the full available panel (2007–2026; ~449,000 institution-quarters, 495 in-window failures) on the deployed frozen thresholds. Because these thresholds are pooled from the same history they are tested on, this is an in-sample-threshold measurement and is reported as corroboration of the out-of-sample result, not as a stricter test.

The out-of-sample result is close to the full-history result, which indicates the rating had genuine crisis-predictive power from pre-crisis information alone rather than benefiting from hindsight. These figures were recomputed under the continuous v2 engine and are within rounding of the prior grade-based model; continuous scoring preserves the model's failure-ranking power.

Calibration

For calibration the empirical failure rate within four quarters is computed for every score band across the full history. A well-calibrated rating shows failure rates that rise monotonically as the strength score falls. The pattern holds with only minor adjacent-band inversions at the top of the scale (where failures are vanishingly rare):

A bank scoring in the lowest band (1–12) failed within a year in more than one case in four (25.9%), versus under 0.03% for banks scoring 67 or above: roughly a nine-hundred-fold difference in realized failure rate. (The single strongest band, 82 and above, saw only 16 failures across 76,000 institution-quarters, so the very top of the scale is best read as "failure is vanishingly rare" rather than as a precise multiple.)

Stability

A useful rating should change because a bank's condition changed, not because of measurement noise. Across ~82,000 consecutive-quarter observations (the most recent five years):

• 89.2% of banks' strength score moved by 2 points or less quarter over quarter.
• 96.9% moved by 5 points or less; the median quarter-over-quarter move was 1 point.

This stability comes from two design choices: the frozen pooled thresholds remove the quarter-to-quarter threshold drift that would otherwise move a score even when a bank's fundamentals were unchanged, and the three-quarter weighted moving average dampens single-quarter noise before it reaches the reported score.

Caveats

• Bank failures are sparse, especially in the post-2015 window, so the confidence intervals around the Gini figures are wide. Treat them as directional evidence of strong rank-ordering, not as exact point estimates.
• The out-of-sample test scores a concentrated stress event (the 2008–2010 failure cluster); its grade-level failure rates run higher across the board than a normal period and are not directly comparable to the full-history calibration above.
• The backtest measures the quantitative scorecard. The qualitative hard floors and adjustments described above are applied on top of the score and are not separately captured in these figures.

Limitations and Disclaimers

Not a credit rating. Counterparty Insight is not a credit rating agency and is not registered as a Nationally Recognized Statistical Rating Organization (NRSRO) under Section 15E of the Securities Exchange Act. The 1–100 strength scores this model produces are independent, quantitative analytical assessments for informational use only. They are not "credit ratings" as defined under the Credit Rating Agency Reform Act, are not a recommendation to buy, sell, or hold any security or to extend or deny credit, and must not be used as the sole basis for any investment, lending, or counterparty decision. The numeric scale is a proprietary risk indicator used for interpretability; it is not affiliated with, endorsed by, or derived from any rating agency.

1. Not a regulatory rating. This model is an independent analytical tool. Actual CAMELS ratings assigned by bank examiners are confidential and incorporate qualitative factors (management interviews, compliance reviews, IT audits) that are not available from public data.

2. Management component is proxy-based. The real CAMELS "M" component evaluates board governance, strategic planning, risk management culture, internal controls, and compliance. The proxy metrics used here (efficiency, growth discipline, diversification, reserve coverage) are correlated with management quality but are not a substitute.

3. Lagging indicators. Financial ratios are backward-looking, reflecting the institution's condition as of the most recent quarterly report date. Rapidly deteriorating conditions may not be reflected until the next filing.

4. Small peer groups. Peer Group 1 (>$100B) contains ~30 institutions per quarter. Pooling 20 quarters mitigates this (~600 institution-quarter records), but the group remains the smallest. Ratios with fewer than 10 valid data points fall back to all-institutions thresholds.

5. Data availability. Some ratios may be null for certain institution types (e.g., thrifts may report different capital metrics). The model handles nulls by excluding them from the averaging, which may result in component scores based on fewer ratios.

6. Threshold staleness. Frozen thresholds are refreshed weekly but are based on the 20 most recent quarters of available FDIC data. When macroeconomic conditions shift significantly (e.g., rapid rate hikes), the pooled thresholds will lag behind the current environment until older quarters roll off the 20-quarter window. This is by design (it prevents short-term volatility from destabilizing scores) but users should be aware that thresholds evolve gradually.

7. Data quality and lineage. Inputs are sourced from public regulatory filings and are subject to the filers' own reporting accuracy, restatements, and framework differences (call report field availability varies by charter type and reporting framework). The pipeline applies field-mapping and reconciliation controls, but residual data-quality risk is inherent to any model built on third-party-reported financials.

8. Not investment advice. This model is for informational and analytical purposes only. It should not be used as the sole basis for investment, lending, or counterparty decisions.