credit-memo 0.1.0

Generate IC credit memos from structured deal inputs — CDFI loans, NMTC deals, and impact investments

credit-memo 📝

Generate IC credit memos from structured deal inputs.

Takes borrower profile, loan terms, financial data, impact metrics, and risk factors as structured Python inputs and generates a complete, formatted Investment Committee credit memo in Markdown or Word (.docx) format.

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Why credit-memo?

Every CDFI analyst, IC committee, and private credit team writes credit memos from scratch in Word. credit-memo standardizes the process — define your deal inputs once, generate a professional IC memo instantly.

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Installation

pip install credit-memo

# For Word .docx output
pip install credit-memo[docx]

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Quickstart

from creditmemo import (
    CreditMemo, DealProfile, BorrowerProfile,
    LoanTerms, FinancialData, ImpactData, RiskFactor,
)

borrower = BorrowerProfile(
    name="Southside Community Health Center",
    borrower_type="nonprofit",
    sector="healthcare",
    state="IL",
    city="Chicago",
    year_founded=2005,
    ceo_name="Dr. Maria Johnson",
    mission="To provide quality healthcare to underserved communities.",
    is_cdfi_certified=False,
)

loan = LoanTerms(
    deal_type="loan",
    amount=2_500_000,
    interest_rate=0.045,
    term_years=10,
    amortization_years=20,
    io_periods=12,
    collateral="First mortgage on real property",
    use_of_proceeds="Acquisition and renovation of community health facility",
    min_dscr_covenant=1.20,
)

financials = FinancialData(
    revenue_y1=2_800_000, revenue_y2=3_000_000, revenue_y3=3_200_000,
    net_income_y1=150_000, net_income_y2=180_000, net_income_y3=210_000,
    dscr=1.35, current_ratio=1.8,
)

impact = ImpactData(
    jobs_created=18, jobs_retained=32,
    patients_served=8500,
    is_low_income_area=True,
    is_nmtc_eligible=True,
    is_minority_borrower=True,
)

deal = DealProfile(
    deal_name="Southside Health Center — $2.5MM Term Loan",
    borrower=borrower,
    loan_terms=loan,
    financial_data=financials,
    impact_data=impact,
    recommendation="approve_conditions",
    prepared_by="Jay Patel",
    prepared_date="2026-05-06",
    fund_name="NCIF Community Lending Fund",
    conditions=[
        "Receipt of final appraisal satisfactory to lender",
        "Evidence of $500k matching funds from borrower",
    ],
)

memo = CreditMemo(deal)
print(memo.to_markdown())
memo.save_markdown("southside_health_memo.md")
memo.save_docx("southside_health_memo.docx")   # requires pip install credit-memo[docx]

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Memo Sections Generated

1. Executive Summary — deal overview, recommendation, key terms table
2. Borrower Profile — organization description, mission, certifications
3. Transaction Structure — loan terms, collateral, covenants, NMTC structure
4. Financial Analysis — historical financials, key ratios, projections
5. Impact Analysis — jobs, units, demographics, eligibility flags
6. Risk Assessment — risk factors by severity with mitigants
7. IC Recommendation — formal recommendation with conditions and signature block

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Deal Types Supported

• loan — Direct loan or line of credit
• nmtc — New Markets Tax Credit investment
• equity — Equity investment
• grant — Grant or forgivable loan
• guarantee — Loan guarantee

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NMTC Deals

from creditmemo import NMTCTerms

nmtc = NMTCTerms(
    nmtc_allocation=10_000_000,
    credit_price=0.83,
    leverage_loan_rate=0.045,
    qlici_a_rate=0.045,
    qlici_b_rate=0.010,
    cde_fee_rate=0.02,
    cde_name="Chicago Development Fund",
    investor_name="US Bancorp CDC",
)

deal = DealProfile(..., nmtc_terms=nmtc, ...)

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Running Tests

PYTHONPATH=. pytest tests/ -v

29 tests across all modules.

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Who This Is For

• CDFI analysts drafting IC memos for loan committee
• Private credit teams standardizing deal documentation
• CDEs preparing NMTC investment memos
• Impact investors documenting community development deals
• Anyone replacing manual Word memo templates with structured Python inputs

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License

MIT 2026 Jaypatel1511