AK

Ark Kapital Financial Model

Two live lending products:

Growth Loan - Long-term non-dilutive loan sized and structured dynamically based on real-time company data. Target borrowers: SaaS companies and others with stable income and underlying profitability. Pie-chart indicators on slide 18 suggest moderate credit risk, partial automation, limited direct competition, and a large companies-in-scope pool.

Marketplace Forward - Short-term automated advance against marketplace receivables (App Store, Steam, etc. - payment cycle ~70 days). Target borrowers: game developers and marketplace sellers. Indicators suggest very low credit risk (near-full pie), near-full automation, moderate competition, and moderate company scope.

AiM platform - Connects via API to borrowers' raw data sources (payment transactions, marketing spend, engagement events, contracts, orders/inventory, accounting events, cloud data warehouses such as Redshift, BigQuery, PostgreSQL, S3, etc.). Uses ML to produce LTV predictions (stated 98%+ accuracy on 12-month LTV) and continuous risk monitoring (dashboards, alerts, APIs back to borrowers).

Not explicitly stated in deck. Inferred from business model:

• Interest / fees on Growth Loans (term loans to SaaS/tech companies).
• Discount fee or factor rate on Marketplace Forwards (cash advance against receivables).
• Channels: proactive data-driven lead sourcing via AiM platform; "large customer pipeline built in 2 months in stealth mode".
• No specific interest rates, fee percentages, or loan sizes disclosed.

• Pipeline: "Significant pipeline in less than 2 months"; "large customer pipeline built in 2 months in stealth mode".
• Product: "Sizeable loan product live".
• Funding: "First large funding being secured at favorable terms".
• Team: "12 star recruitments across all relevant disciplines".
• Platform: "Predictive tech platform supporting major data platforms"; "Loan decisions significantly improved by tech platform".
• ML accuracy: 98%+ accuracy on 12-month LTV predictions.
• No revenue figures, loan volumes, number of funded companies, or specific pipeline counts disclosed.

Positioned against:

• Traditional bank lending - unable to assess tech risk, inflexible structures.
• VC equity - dilutive, expensive, available to only a minority of companies.
• Other fintech lenders - implied by "mispriced risk in the market" framing.

Stated moats:

• Proprietary AiM data platform with low-level API integrations to raw event-level data (not aggregated financials).
• ML at user/micro-cohort level - claimed differentiation vs. competitors using Excel models, ARR multiples, or contract financing.
• Scalable credit facilities secured at launch ("profitable lending from day 1").
• Team pedigree: Spotify analytics, EQT Motherbrain, Tink, Nordea, SEB, UC credit bureau.

Founders:

• Oliver Hildebrandt (CEO, 29) - built and scaled 6 companies (4 fintechs), combined €100M revenue / 500 employees.
• Henrik Landgren (CPТО & Founder) - built Spotify analytics team; built AI investment platform Motherbrain at EQT; founded EQT Ventures & Growth data-driven VC.
• Axel Bruzelius (COO & Founder) - built first debt-driven startup unit at Nordea; lent to hundreds of tech companies with no credit losses.

Key hires:

• Elin Bäcklund (CTO) - Head of Motherbrain dev team (25 people), Forbes 30 Under 30.
• Jens Larsson (Head of Analytics) - Tink analytics head; prior Spotify + Google data.
• Julia Erhardt (CFO) - SEB Head of Debt IR (>€10B annual fundraising), Chief Retail Bank at Hoist Finance.
• Tim Bisander (Head of Credit) - 20+ yrs, Head of Analytics at Swedish credit bureau UC, Marginalen Bank, Skandia mortgage.
• Anders Hising (Head of Structuring) - 14 yrs leverage finance & corporate debt at Nordea.

Existing angel investors: Jacob de Geer, Hjalmar Winbladh, Patrick Söderlund, Johan Bergqvist, Gabriella Sahlman.

Funding ask: ~€15M Seed.

• Lead investor(s): €8–10M.
• Existing angels pro-rata: €3M.
• Selected new hand-picked angels: €2–4M.

Use of funds (stated):

• Hire 40 FTEs across tech, lending, and growth teams.
• Expand to multiple European markets.
• Build out scalable AI platform.
• Finance loans against debt facility (i.e., equity funds equity cushion / credit enhancement, not loan principal directly).
• Drive explosive customer growth.

Archetype + why: Specialty lender / balance-sheet lending P&L + loan-book model. ArK is a credit business: revenue is interest and fees on loans originated; costs are cost of funds (debt facility), OpEx (tech + team), and credit losses. The correct model archetype is a lending / specialty finance model: loan book build (originations, repayments, outstanding balance), net interest margin, provision for credit losses, and an operating P&L sitting above. The AiM platform has SaaS-like cost structure (data infra, ML team) but is not a revenue line - it's the underwriting engine. Do not model as SaaS ARR.

Forecast horizon & granularity: Monthly for Year 1–2 (loan book ramps fast post-seed); quarterly summary for Years 3–5. 5-year forecast total. Two product lines modelled separately (Growth Loan, Marketplace Forward) given different duration, credit risk, and automation profiles.

Key drivers & assumptions:

*Loan book - Growth Loan (long-term):*

• Number of new borrowers per month.
• Average loan size per borrower.
• Average loan tenor.
• Interest rate / fee yield.
• Net interest margin after cost of funds.
• Monthly repayment / amortization profile.

*Loan book - Marketplace Forward (short-term):*

• Number of advances per month.
• Average advance size.
• Discount rate / factor fee.
• Turnover / recycling rate.

*Funding / balance sheet:*

• Equity raised: €15M seed.
• Debt facility: size not disclosed.
• Cost of debt facility.
• Loan-to-facility utilisation ramp.

*Credit / risk:*

• Default / loss rate.
• Provision coverage ratio.

*OpEx:*

• Headcount ramp to 40 FTEs;.
• Tech infrastructure (data platform, cloud, APIs).
• Overhead (office, legal, compliance, audit).

Scenarios (Base / Bull / Bear - which variables flex):

• Bear: Slow pipeline conversion (50% of base borrower ramp), higher credit losses (3% Growth Loan / 1.5% Forward), debt facility at 3x equity, higher cost of funds (8%).
• Base: Mid-case borrower ramp, 1.5% Growth Loan losses / 0.5% Forward, 5x leverage, 7% cost of funds.
• Bull: Fast pipeline (150% of base), 0.5% loss rate, 8x leverage, 6% cost of funds, faster European market expansion.
• Flex variables: origination volume, average loan size, loss rate, leverage ratio, cost of funds.

Required sheets / outputs:

1. Assumptions - all drivers, clearly flagged vs..
2. Loan Book - Growth Loan - monthly originations, repayments, outstanding balance, interest income, fees.
3. Loan Book - Marketplace Forward - monthly volume, advance balance (short-dated), discount income.
4. Funding / Liabilities - equity, debt facility drawdown, utilisation, interest expense.
5. P&L - gross interest income, cost of funds, NIM, credit provisions, gross profit, OpEx, EBITDA, net income.
6. Balance Sheet - loan receivables, cash, debt facility, equity.
7. Cash Flow - operating CF, net new originations (investing), debt drawdowns (financing), ending cash / runway.
8. KPI Dashboard - loan book size, NIM%, loss rate, cost/income ratio, runway months, cumulative borrowers.
9. Scenario Toggle - bear/base/bull selector feeding Assumptions sheet.