Non-interest demand deposits (DDA) are the cheapest, stickiest source of funding. Banks with DDA >30% of deposits have a durable franchise advantage that shows up in CoF and NIM. At the other extreme, banks where time deposits (CDs) exceed 40% of funding are exposed to hot-money flight when rates shift.
When uninsured deposits exceed 40% of the book, a bank is structurally exposed to a run. QoQ direction matters: shrinking uninsured % signals management is actively de-risking. Expanding uninsured % at banks already above 30% is the pattern that preceded the 2023 regional bank failures.
| Bank | Assets $M | Uninsured % | QoQ Chg pp | Deposits $M | Vuln Tier |
|---|
QoQ deposit outflow crossed with cost-of-funds change. Banks losing deposits AND seeing CoF rise are in a liquidity squeeze — they’re paying up to hold what remains. Banks growing deposits while CoF is flat or falling are winning share at someone else’s expense.
| Bank | Deposits $M | QoQ % | CoF Chg bp | State |
|---|
| Bank | Deposits $M | QoQ % | CoF Chg bp | State |
|---|
Each bank’s CoF is FFIEC’s officially-computed UBPRE013 (Cost of All Interest-Bearing Funds, annualized using average balances). Peer CoF is the median within the bank’s SPC cohort (size + geography + specialty) where the cohort has ≥5 members, otherwise the fleet median. Banks paying >50bp above peer are overpaying for funding — candidates for a treasury-services or deposit-repricing engagement. Banks paying >50bp below peer are franchise standouts worth benchmarking.
| Bank | CoF % | Peer % | Gap bp | Assets $M |
|---|
| Bank | CoF % | Peer % | Gap bp | Assets $M |
|---|
What differentiates banks is deposit beta — how fast CoF rises with the Fed. For Q1 2026 we proxy deposit beta from deposit-mix stickiness: non-interest DDA% minus time-deposit% of total deposits. Banks at the top quartile (lots of DDA, little time) are low-beta (funding reprices slowly). Banks at the bottom quartile (little DDA, heavy time deposits) are high-beta (rate-sensitive).
| Quartile | Banks | Median stickiness (DDA% − time%) | IQR (p25–p75) | Range |
|---|---|---|---|---|
| ■ Low (best) | 922 | −10.8 pp | [−17.7, −0.1] | −22.8 to +99.3 |
| ■ Mid | 1,842 | −36.6 pp | [−42.5, −30.6] | −48.4 to −22.8 |
| ■ High (worst) | 922 | −56.8 pp | [−64.0, −52.0] | −85 to −48.3 (excl. specialty outliers) |
| Bank | State | Assets $M | Deposits $M | DDA % | Uninsured % | CoF % | CoF vs Peer bp | QoQ % | Vuln | Vuln Driver Mix (click row for detail) | Franchise |
|---|
Click any row in the leaderboard to see vulnerability drivers, peer comparison, funding mix, and risk breakdown for that bank — right here, no scrolling.
Total vulnerability score breaks down into 5 independent risk drivers. Two banks can both score 75 RED for very different reasons — one drowning in uninsured deposits, another paying way above peer for funding. The composition determines the remedy. Click any bar to see the bank's full breakdown.
| Bank | Vuln | Driver mix (click for detail) | Primary driver | Assets $M |
|---|
Each region is colored by the share of its banks that scored RED on the deposit-vulnerability tier. Cluster bubbles show the bank count and that share at a glance. Zoom in and state fills give way to county fills, then individual bank pins. Switch the metric to avg score for a smoothed view, or franchise for the deposit-quality lens. Hover any region or pin for details; click a pin for full analytics.
The Deposit Vulnerability Score adapts the deposit-stress dimension of Basel III's Liquidity Coverage Ratio (LCR) — the same conceptual framework U.S. regulators apply to banks >$50B in assets — and applies it to community banks, where LCR doesn't formally apply. The Fed's CCAR/DFAST stress tests use a similar set of inputs (depositor-stratified run-off rates, wholesale-funding mix, time-deposit reliance) to assess deposit-flight risk under adverse scenarios.
All inputs are standard regulatory measures sourced from FFIEC Call Reports and UBPR:
What's specific to Statum is the composite synthesis: which five drivers to combine and how they're weighted. The driver weights (25/20/20/20/15 for V1–V5; 30/25/20/15/10 for F1–F5) are documented below. The Risk Drivers tab provides a per-bank driver attribution that decomposes each bank's score back to its 5 driver primitives so the synthesis is fully auditable against the bank's own call report.
Tier cutoffs — cross-sectional, single-quarter: Rather than fixed score thresholds (the previous approach), tiers are now defined by distance from the fleet median for the current quarter, measured in units of MAD (Median Absolute Deviation). MAD is the robust analogue of standard deviation — it's not pulled around by outliers (the very banks the score is trying to surface).
The actual numeric cutoffs for this quarter are loaded with the data and printed in the page banner. They will change each quarter as the underlying distribution shifts.
State and county aggregation — asset-weighted: Each state's and each county's number on the heatmap is the asset-weighted mean of the bank vulnerability scores HQ'd there: Σ(score × assets) / Σ(assets). This answers the question "how much money is at risk in this state?" rather than "what does the average community bank in this state look like?" An equal-weighted mean (the previous approach) gave a tiny state with three small banks the same statistical voice as a state with three hundred banks of vastly different sizes — misleading at the geographic level. Asset-weighting matches the convention used by Fed surveillance and standard industry peer benchmarks.
The per-bank vulnerability and franchise scores themselves are not asset-weighted — each bank is scored on its own profile. Only the geographic aggregates (state & county fills on the heatmap, state_vuln_avg in the data) use asset weighting. Cluster bubbles on the map use the same asset-weighted aggregation across the banks within the cluster.
Higher = more exposed. Five weighted drivers. Each driver score is the bank's percentile rank within the fleet on that dimension, multiplied by the driver's max points. Cross-sectional, single-quarter; no fixed thresholds. The composite tier cutoffs are computed separately at each aggregation level (bank / state / county) using median ± k·MAD on that level's own distribution — see the methodology card above for the live numeric cutoffs.
Higher = better. Five weighted drivers, same percentile-rank scoring approach as Vulnerability but inverted for direction (low CoF, low uninsured, low non-core, high DDA, high deposit growth all score higher).
Scope: US FDIC-insured banks with assets ≥ $100M AND deposits ≥ $50M (Q1 2026 FDIC Call Report). At smaller scale, deposit ratios reflect single-account effects rather than systemic risk. 648 banks excluded by this floor.
Data sources:
Deposit-beta quadrants on the rate-sensitivity tab are derived from fleet quartiles of deposit-mix stickiness (DDA% minus time-deposit%); the irr_deposit_beta_inference field in the Q1 SOT extracts is uniformly "MODERATE_BETA" so cannot be used directly.
On the State Heatmap tab, click any state to open a side panel listing every bank HQ'd there (sorted by assets). Toggle individual banks on/off to see how each contributes to the state's asset-weighted aggregate. Live "Δ from full state" shows the recomputed mean. Excluding individual banks is an investigative tool — map coloring stays at full-state values. Useful for stress-testing claims like "Hawaii's number is dominated by First Hawaiian Bank" or "Utah's average reflects Goldman Sachs Bank USA's profile".