Hum Capital Financial Model

• ICM platform: companies upload financials from SaaS systems of record (QuickBooks, Stripe, etc.); Hum's data pipeline auto-structures, benchmarks, and analyzes the data.
• Companies get an "investor's view" of their own business - runway, revenue, IRR by marketing channel, peer benchmarks - before pitching.
• Investors get pre-screened deal flow with standardized financial analytics, reducing due diligence time.
• Positioning: "Kayak.com for private capital" - companies receive multiple competing term sheets (avg. 3.3 per company).
• Network effects: more companies → more diverse investor demand → lower cost of capital → more companies join.
• Technical moat: Data Acquisition Engine → Cross-data Standardization Suite → Ground Truth Data Dictionary; covers >220 industries from first dollar of revenue to IPO.

• Institutional credit market (TAM framing): $1.5T market size by 2025, generating $44B in fees by 2025.
• Fee pool trajectory (gross margin $ from chart, 2021–2025): $28B → $32B → $35B → $39B → $44B.
• Y-axis label on chart is "Gross Margin $" - implies Hum frames the TAM as addressable fee/gross-profit pool, not total capital deployed.
• Hum's share shown as a thin blue sliver vs. "Rest of the market" - early penetration stage as of 2021.

• Primary: success fees on closed financings (marketplace take-rate model). Deck references "$400M+ closed financings in 2021 (YTD)" and "$3.5B GMV of active financings."
• The fee rate / take-rate percentage is not disclosed in the deck.
• Possible secondary: SaaS subscription or analytics fees for companies (the platform provides ongoing analytics, suggesting potential SaaS layer) - typical for this model but not explicitly stated in deck.
• Investors may also pay for deal flow access or AUM deployment tools - Not in deck.

All figures from slide 5 KPI dashboard (image-verified):

• Total companies on ICM: 2,000+
• Daily active companies: >500
• Revenue of daily-active companies (TTM): >$10B (aggregate customer revenue, not Hum's own revenue)
• Gross Monetary Value (GMV) of active financings: $3.5B
• Closed financings in 2021 (YTD): >$400M
• Avg. competitive term sheets per company: 3.3
• Active institutional investors on ICM: 250+
• Investor AUM focused on Hum's universe: $11B+
• Note: Hum's own revenue (i.e., fees earned) is not disclosed; only GMV and closed financing volume are shown.

• Platform dashboard (slide 7) shows sample company metrics (7.6 mo runway, $2.7M revenue, 133% avg. IRR on sales & marketing spend) - these are illustrative customer data, not Hum's own economics.

• Competitive framing: "without Hum" = cold outreach to individual investors (weeks of meetings, no data leverage).
• No direct competitors named in the deck.
• Moats stated:

1. Data moat: millions of observed financial data points across >220 industries; proprietary Cross-data Standardization Suite.
2. Network effects: two-sided marketplace with self-reinforcing flywheel (more companies → more investors → better capital options → more companies).
3. Team: blend of Stanford engineers + Wall Street investors (KKR CFO, Credit Suisse quant, Oaktree legal, Twitch engineering).

Team (slide 10):

• Blair Silverberg - Founder / CEO (Stanford Engineering)
• Csaba Konkoly - Co-Founder / President (Commonwealth)
• Chris Olivares - Co-Founder / CTO (Stanford Engineering)
• Scott Brown - CMO (Google)
• Ken Eagle - CFO (KKR)
• Yotam Troim - CPO (Fundbox)
• Emily Stephens - Special Legal Advisor (Oaktree)
• Chris Dolezalek - EVP Engineering (Twitch)
• David Wood - Head of Quantitative Strategies (Credit Suisse)

Archetype + why: Marketplace GMV / take-rate revenue model with SaaS analytics overlay.

• Hum earns fees on financed deal flow (GMV × take-rate = revenue), which is classic marketplace mechanics. The analytics/data platform layer may generate a subscription revenue stream, but the primary driver is closed financing volume.
• Two revenue lines to model: (1) Financing fees = GMV closed × take-rate; (2) Platform SaaS / data subscription if that layer exists.

Forecast horizon & granularity: 5 years (2022–2026), monthly in Years 1–2, quarterly in Years 3–5. As of deck date (2021), the business is in early-growth; monthly granularity needed to track GMV pipeline conversion.

Key drivers & assumptions:

*Supply side (companies):*

• Total companies on ICM: 2,000+; growth rate ~50–80%/yr early, tapering to 25%/yr by Year 3 as market matures
• Daily active company (DAC) rate: >500/day implied ~25% DAC/total ratio; stable at 20–25%
• Average revenue of active companies (TTM): >$10B across 500+ DACs = ~$20M avg revenue per DAC; stable mix - mix of seed-stage to growth-stage companies

*Demand side (investors):*

• Active institutional investors: 250+; grows ~30%/yr as platform track record builds
• Investor AUM on platform: $11B+; grows with investor count and AUM per investor

*GMV & deal flow:*

• GMV pipeline (active financings): $3.5B; represents ~6–9 month pipeline; conversion to closed ~10–15% per quarter
• Closed financing volume (YTD 2021): >$400M; annualized ~$600–800M
• Avg. deal size: ~$2–5M per financing (SMB/growth-stage debt/equity)
• Avg. term sheets per company: 3.3 - indicates strong investor competition; maintained as quality signal

*Revenue:*

• Take-rate on closed financings: 1–3% of deal value (typical range for private capital placement agents / fintech marketplaces; exact rate not disclosed)
• Revenue = Closed GMV × take-rate; implied 2021 run-rate: $600–800M × 2% = $12–16M ARR
• SaaS/analytics subscription: $0–5K/month per active company; not confirmed in deck - treat as upside scenario only

*Costs:*

• Headcount: engineering-heavy (Stanford + Twitch pedigree); ~30–60 FTEs at this stage
• COGS: primarily cloud/data infrastructure; 20–30% gross margin drag on GMV fees
• S&M: high relative to revenue in early years; company acquisition cost likely low (companies come for analytics, not just capital)

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

• Base: Closed GMV grows ~60%/yr, take-rate 1.5–2%, SaaS layer minimal
• Bull: Take-rate expands to 2.5–3% as platform proves value; closed GMV grows >100%/yr; SaaS subscription layer launches; international expansion
• Bear: Take-rate compressed by competition to <1%; GMV growth slows to 25–30%/yr; DAC rate declines; institutional investors pull back in credit downturn

Required sheets / outputs:

1. Assumptions - all drivers with / tags
2. GMV Model - pipeline build → conversion → closed GMV by quarter
3. Revenue - take-rate fees + optional SaaS line
4. P&L - revenue, COGS (infra), gross profit, S&M, R&D, G&A, EBITDA
5. Headcount plan - tied to growth milestones
6. Cash flow & runway
7. KPI dashboard - GMV, DACs, companies on platform, investors, take-rate, CAC (when data available)
8. Scenarios sheet (Base / Bull / Bear toggles)