Defacto Financial Model
Fintech Startup Financials (Free Excel Download)
Embedded short-term SMB lender offering instant, uncollateralized working capital loans via API integrations into accounting, banking, invoicing, and marketplace platforms across Europe.
professionals from Deloitte
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About this model
Defacto provides embedded, short-term working-capital loans to European SMBs through accounting, banking, invoicing, and marketplace partners. Its API-led product makes funding decisions in seconds and pays out quickly, using live business data rather than slow traditional underwriting.
The company originates uncollateralised loans with short maturities, averaging about 48 days, and funds them through an institutional debt structure. Partner distribution and repeat borrower behaviour are central to keeping customer acquisition low and origination velocity high.
The model should track partner channels, eligible borrowers, loan originations, average balance, yield or fees, repayment turnover, and credit losses. Funding cost, SPV debt capacity, servicing expense, and default rates belong in the core P&L because this is a lending business, not a SaaS ARR model.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Defacto
getdefacto.com
How to build a detailed financial model for Defacto
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Defacto model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Short-term (max 120 days, average 48-day maturity), uncollateralized working capital loans to SMBs.
- Loan decisions in 5 seconds vs. >30 days for traditional banks; funds disbursed instantly vs. >1 week.
- Does NOT purchase the underlying invoice (unlike factoring), enabling both AP and AR financing.
- Requires AP/AR invoices to draw on credit line; invoices used for underwriting context.
- API-first; credit is surfaced in-context inside partner platforms (e.g., Qonto, Pennylane).
- Three proprietary capabilities owned end-to-end: embedded distribution, ML underwriting, financing & servicing (SPV, payments, reconciliation).
Revenue model
- Interest and/or fee income on short-term loans originated to SMBs.
- Loans are max 120 days, average 48-day maturity - very high turnover of loan book.
- Revenue (ARR) is referenced but exact ARR figures are redacted in the deck (shown as XXX/XM€).
- Debt cost, default cost, and operations cost are the three gross margin drivers.
- Funding structure: SPV with institutional debt; Citi mentioned as new debt facility that halved funding cost.
- GTM: partner-embedded (accounting, banking, invoicing, marketplace platforms) - lowest CAC in market is the claim.
Traction & metrics
- May 2021: founded, 3M€ pre-seed (GFC + Headline).
- January 2022: product go-live.
- March 2022: first partner onboarded.
- April 2022: 15M€ Series A (Northzone + GFC + Headline).
- May 2023: 190M€ cumulative originated.
- Origination velocity: 12 months to reach 100M€, 6 months to reach 200M€, 4 months to reach 300M€ cumulative originated - i.e., ~300M€ cumulative by ~Oct 2023.
- CSAT of 82%; 80% return rate for a second loan.
- Outstanding loan book growing (3 periods shown: Oct, May, Dec 2023); other partners grew x3 in 6 months - absolute figures redacted.
- ARR projection from May '23 to Dec '25 shown as bar chart with 5 periods - all values redacted.
- Yearly burn rate chart shown (years redacted) - narrative: decelerating burn toward profitability.
- International share outside France: Germany, Spain, Netherlands, Belgium - exact % redacted.
Unit economics
- Gross margin components: Gross Margin line, less Default cost, Operations cost, Debt cost.
- Gross margin improving '23→'25: funding cost halved (Citi facility), default cost to reduce via ML, ops cost to reduce via automation - specific % figures redacted.
- Claim: lowest CAC in market due to embedded GTM.
- ARR per head (productivity metric referenced) - figure redacted.
- Average loan maturity 48 days → implies loan book turns ~7.6x per year.
Competition / moat
- Vs. traditional banks: >10h application, >30 days processing, >1 week fund access.
- Vs. factoring: no invoice purchase, avoids invoice ownership disputes; customer cited paying ~15K€/yr in factoring fees.
- Vs. other lenders: competitors rely on declarative/financial statement data, external scoring (Ellisphère), 6-month data lag providers (Codat), human-driven review.
- Defacto moat: live transactional data access, event-based architecture, cross-partner borrower metrics flywheel, proprietary ML trained on short-cycle loan data.
- Partners named (partially): Qonto, Pennylane; other large partners redacted.
Team & funding ask / use of funds
- Founders: Jordane Giuly (Product & Finance), Marco Gires (Tech & Data), Morgan O'hana (Partnerships & Marketing).
- Raised to date: 3M€ pre-seed (May 2021) + 15M€ Series A (April 2022) = 18M€ equity.
- Debt facility: Citi (SPV / warehouse) - size not disclosed.
- Funding ask: Not explicitly stated in deck. Implied Series B given stage and 2023 date.
Recommended financial model
- Archetype + why: Lending / specialty finance P&L model (origination-based ARR + net interest margin). Defacto is an originating lender with a revolving short-duration loan book, not a SaaS business. The correct model tracks: (a) loan book outstanding balance, (b) origination volume, (c) net interest/fee yield on book, (d) credit losses (default rate), (e) funding/debt cost, (f) opex. ARR is a proxy metric they use internally but the underlying economics are lending economics.
- Forecast horizon & granularity: Monthly for Years 1–2 (2023–2024), quarterly for Year 3 (2025). Three years total, matching the deck's ARR chart horizon (May '23 – Dec '25).
- Key drivers & assumptions:
| Driver | Value | Source |
|---|---|---|
| Cumulative origination at model start (May 2023) | 190M€ | - |
| Average loan duration | 48 days | - |
| Max loan duration | 120 days | - |
| Loan book turns per year | ~7.6x (365/48) | derived from avg maturity |
| Outstanding book size at start | ~25–30M€ | estimated from 190M€ cumul ÷ 7.6x turns, scaled to a point-in-time snapshot |
| Monthly origination growth rate (2023) | ~15–20% MoM | inferred from 100M€ in 12 months → 200M€ in next 6 months → 300M€ in next 4 months acceleration |
| Net yield on loan book (interest + fees) | 8–12% p.a. | typical embedded SMB lender in Europe; specific rate not disclosed |
| Debt cost (cost of funds) | ~5–6% initially, ~3% post-Citi | "halved with new fund"; ECB rate environment context |
| Default rate (credit loss) | 1.5–3% p.a. | "to reduce by X% via ML" implies currently elevated; short-duration reduces severity |
| Operations cost % of origination | 1–2% | "further automation will cut ops costs" |
| CSAT / repeat borrower rate | 80% return for 2nd loan | - |
| Headcount growth | lean; "10X engineers", automation-first | |
| Target gross margin trajectory | Expanding '23→'25 | specific % redacted |
| Target ARR at Dec 2025 | Redacted (XM€) | - |
| International mix (non-France) | Redacted (XX%) | - |
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: Steady partner expansion, monthly origination growth ~10–15% MoM decelerating to 5–8% by 2025; default rate stable; Citi facility executes.
- Bull: New large partner signed (Germany/Netherlands expansion), origination acceleration, default rate falls faster with ML, gross margin expansion ahead of schedule.
- Bear: Partner concentration risk (single partner delay), ECB rate rise squeezes net spread, elevated defaults from SMB credit cycle, origination growth slows to <5% MoM.
- Key flex variables: origination growth rate, net yield, default rate, funding cost, headcount.
- Required sheets / outputs:
- Assumptions - all drivers with scenario toggles
- Loan Book - monthly: opening balance, new originations, repayments (maturity-based), closing balance
- P&L - net interest income, fee income, credit losses, gross profit, opex (headcount + tech + ops), EBITDA, net income
- Gross Margin Bridge - debt cost / default / ops cost waterfall by year (mirrors slide 16)
- ARR Schedule - annualized net revenue run-rate (management metric)
- Burn & Cash - equity cash consumed; runway to profitability
- KPI Dashboard - cumulative origination, active loan book, gross margin %, ARR, ARR/head, default rate
Frequently asked
Is the Defacto financial model free?+
Yes. The Defacto model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Defacto's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
Created by ex-finance professionals
Hey, I’m Alex and I created Finamodel.
Over my years in the finance industry I kept building the same models over and over again. Same structure, same assumptions, different logo. So I started building frameworks to turn them into clean, reusable templates.
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