cdfi-benchmark 0.2.0

CDFI and MDI peer benchmarking tool using FDIC call report data — NIM, efficiency ratio, ROAA, CET1, and more

cdfi-benchmark 📊

CDFI and MDI peer benchmarking tool using FDIC call report data.

Pull call report financials for any FDIC-insured CDFI or MDI, compute key performance metrics, build a peer group of similar institutions, and generate a benchmarking report — using the free FDIC BankFind Suite API, no API key required.

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Why cdfi-benchmark?

CDFI banks and MDIs benchmark their performance against peers manually — pulling call report data from FFIEC, computing ratios in Excel, and building comparison tables by hand. cdfi-benchmark automates the entire workflow in Python.

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Installation

pip install cdfi-benchmark

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Quickstart

from cdfibenchmark import (
    get_financials, build_peer_group,
    generate_report, summary_table,
)

# Pull call report data for Broadway Federal Bank (CERT 57542)
institution = get_financials(cert=57542)

# Build peer group — similar asset size, no API key needed
peers = build_peer_group(institution, same_state=True)

# Generate benchmarking report
report = generate_report(institution, peers)
print(report)

# Get results as DataFrame
df = summary_table(institution, peers)

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Sample Data (No API Required)

from cdfibenchmark import build_sample_peer_group
from cdfibenchmark.data.schema import InstitutionProfile

institution = InstitutionProfile(
    cert=57542,
    name="Broadway Federal Bank",
    city="Los Angeles",
    state="CA",
    report_date="20241231",
    total_assets=655_000,
    total_deposits=520_000,
    net_loans=380_000,
    net_income=1_950,
    interest_income=28_000,
    interest_expense=8_000,
    non_interest_income=3_500,
    non_interest_expense=22_000,
    total_equity=48_000,
    tier1_ratio=12.2,
)

peers = build_sample_peer_group(institution)
report = generate_report(institution, peers)
print(report)

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Metrics Computed

Metric	Description	Benchmark (Strong)
NIM	Net Interest Margin	>= 3.5%
Efficiency Ratio	Non-interest expense / Revenue	<= 60%
ROAA	Return on Average Assets	>= 1.0%
ROAE	Return on Average Equity	>= 10%
Tier 1 Capital Ratio	Regulatory capital ratio	>= 12%
Loans-to-Deposits	Loan utilization	<= 80%
NPL Ratio	Non-performing loans / Gross loans	<= 1.0%
Reserve Coverage	Loan loss reserve / NPLs	>= 100%

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Asset Size Buckets

• micro — Under $50MM
• small — $50MM to $250MM
• medium — $250MM to $1B
• large — $1B to $5B
• mega — Over $5B

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Data Source

FDIC BankFind Suite API — free public API, no authentication required. Data covers all FDIC-insured institutions with quarterly call report data since 1934.

https://banks.data.fdic.gov/api

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Error handling

The data layer fails loud. In an early-warning / anomaly-detection pipeline a silently-empty or fabricated result reads as "nothing anomalous" and masks the real problem, so the FDIC fetchers raise typed errors instead of swallowing failures:

• FDICAPIError — a transport problem: the request never produced a usable response body. Network/timeout errors, non-2xx HTTP status, and JSON decode failures all raise this.
• FDICResponseError — the response decoded but its structure is wrong, either at the envelope level (the top-level data key absent, null, or not a list) or at the field level inside a record: a record missing its CERT identity, a CERT that isn't int-coercible, or any core/optional financial field that is present but not numeric. A bad record is never coerced into a phantom cert=0 bank or a fabricated 0.0.
• Legitimately empty is not an error. A successful request that returns zero rows ({"data": []}) returns the empty value for that fetcher — None, an empty DataFrame, or [] — and does not raise.

Missing-but-not-garbage fields inside an otherwise valid record are kept, not dropped: an absent core financial (e.g. ASSET) becomes NaN (unknown — it propagates to any metric computed from it rather than fabricating 0.0), and an absent optional ratio (e.g. RBCT1J) becomes None. A real present 0.0 is preserved as 0.0.

Both error types subclass CDFIBenchmarkError, so callers can catch the contract broadly or distinguish "the API is unreachable" (FDICAPIError) from "the API changed its shape" (FDICResponseError):

from cdfibenchmark import FDICAPIError, FDICResponseError

try:
    institution = get_financials(cert=57542)
except FDICAPIError:
    ...   # transport/HTTP/decode failure — retry or alert
except FDICResponseError:
    ...   # wrong-shape or present-but-garbage field — contract problem

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Running Tests

PYTHONPATH=. pytest tests/ -v

79 tests across all modules.

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Who This Is For

• CDFI banks and credit unions benchmarking against peers
• MDI management teams preparing board reports
• CDFI Fund analysts reviewing institution performance
• Impact investors evaluating CDFI bank investments
• Researchers studying community banking performance trends

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License

MIT 2026 Jaypatel1511