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.
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.
pip install credit-memo
# For Word .docx output
pip install credit-memo[docx]
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]
- Executive Summary — deal overview, recommendation, key terms table
- Borrower Profile — organization description, mission, certifications
- Transaction Structure — loan terms, collateral, covenants, NMTC structure
- Financial Analysis — historical financials, key ratios, projections
- Impact Analysis — jobs, units, demographics, eligibility flags
- Risk Assessment — risk factors by severity with mitigants
- IC Recommendation — formal recommendation with conditions and signature block
- loan — Direct loan or line of credit
- nmtc — New Markets Tax Credit investment
- equity — Equity investment
- grant — Grant or forgivable loan
- guarantee — Loan guarantee
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, ...)
PYTHONPATH=. pytest tests/ -v
29 tests across all modules.
- 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
MIT 2026 Jaypatel1511