# Seyna Financial Model

Insurance infrastructure platform - risk carrier + SaaS - enabling brokers and retailers to build, distribute, and manage insurance products.

- Canonical: https://finamodel.com/startups/seyna
- Excel download: https://finamodel.com/startup-models/seyna.xlsx
- Category: InsurTech
- Model type: Insurance GWP
- Funding round: Series A
- Funding: $37M
- Founded: 2022
- Geography: France (Seed phase); Europe expansion planned at Series A (Freedom of Services filed with ACPR) [DECK, slide 7].
- Customer: B2B

## About the company

Seyna combines a licensed insurance carrier with SaaS infrastructure for brokers and retailers. Its platform supports white-label products, distribution, policy administration, claims, compliance, data processing, and reporting.

Revenue comes from insurance premiums because Seyna carries risk, alongside SaaS fees paid by distribution partners for its operating platform. Reinsurance partners transfer a portion of underwriting exposure while brokers and retailers bring the customer flow.

The model separates carrier GWP from platform ARR. Partners, policies, premium, loss ratio, reinsurance, software fees, expansion, and churn determine revenue. Underwriting performance, broker adoption, capital, and claims operations drive profit.

## What's included

- 5-year monthly revenue build with stage-appropriate growth assumptions
- Full P&L, headcount plan, and operating-expense schedule
- Cash-flow statement, runway, and burn-rate tracking
- Valuation via exit multiple with a DCF cross-check
- Returns analysis with MOIC and IRR
- Unit economics including CAC, LTV, payback, and cohort retention

## Product & value proposition

- Seyna acts as a licensed risk carrier + software platform - the Shopify analogy for insurance.
- Four product pillars:
  1. White-label insurance products (online catalog, dynamic pricing, self-service).
  2. SaaS to run business: API data processing, BI/reporting, market data, compliance automation.
  3. SaaS for distribution: plug-and-play distribution software (physical and digital), contract management.
  4. SaaS for policies & claims: end-to-end policy/claims management, cash transactions, fraud/AML integrations.
- Key claimed efficiency: 36x faster product creation/updates vs. incumbents.
- Brokers benefit: more innovation, more sales growth, increased margins (lower tech spend, fewer errors).

## Market

- TAM: €565bn - P&C and Life insurance excluding savings & pensions.
  - France: €85bn.
  - Europe ex-France: €480bn.
  - Sub-split: P&C €416bn, Health €149bn.
- Market growth: CAGR 4% (2015–2019). Sources cited: FFA & EIF.

## Revenue model

- Dual revenue stream:
  1. Insurance premium revenue (GWP): Seyna is the risk carrier; earns insurance premiums from policyholders via broker/retailer distribution channel. Reinsurance partnerships (Swiss Re, Munich Re, Hannover Re, Scor) transfer risk.
  2. SaaS fees: Brokers/retailers pay for the platform (distribution, policy management, claims, BI tools). Metric used in deck: ARR per broker.
- Channel: B2B2C - brokers and retailers as distribution partners, who sell embedded or D2C insurance to end customers.
- Verticals at Seed stage (France): Unpaid rent & surety, cancellation, embedded motor, pet insurance, breakdown/breakage/theft.

## Traction & metrics

- Named broker clients (migrating to Seyna):
  - Verspieren - embedded on Electrodepot (retailer), vertical: Breakage. ARR:.
  - Garantme - D2C + distributors/agencies, product: Rental insurance. ARR:.
  - Phenomen - FNAC Billeterie (online and offline), vertical: Ticket Cancellation. ARR:.
- Additional traction bullets on slide 7 are redacted (two bullet points blacked out).
- Reinsurance partnerships established: Swiss Re, Munich Re, Hannover Re, Scor.
- Existing investors: GFC, Allianz, Financière Saint James (family office).
- All quantitative traction metrics (total ARR, GWP, policy count, NPS, retention) are absent or redacted.

## Competition / moat

- Moat claims:
  - Licensed risk carrier capacity (regulatory barrier to entry - ACPR-approved, FoS filed for EU).
  - Reinsurance marketplace already operational (connecting policy holders to balance sheets at scale).
  - Data-sharing model: Seyna shares data openly with all market players - competing on service quality, not data lock-in.
  - 36x speed advantage in product creation vs. incumbents.
- Competitive set: Not named. Deck positions incumbents as slow/fragmented (multitude of independent systems).

## Team & funding ask / use of funds

- Investors to date: GFC, Allianz, Financière Saint James.
- Funding ask / round size: Redacted on slide 6 (Series A column blacked out).
- Use of funds: Not explicitly stated, but roadmap implies: +3 new verticals (Event Cancellation, Health, Protection) + 1 new country launch at Series A.

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## Recommended financial model

- **Archetype + why:** Insurance GWP + SaaS ARR hybrid model. Seyna operates as a licensed risk carrier (generating GWP, subject to loss ratios and combined ratios) while also charging SaaS subscription/ARR fees from broker/distributor clients. Neither a pure SaaS nor a pure insurance model - needs both P&L structures. Closest archetype: InsurTech platform with separate GWP P&L and SaaS ARR schedule, feeding into a 3-statement model.

- **Forecast horizon & granularity:** 5 years (Year 1–2 monthly, Year 3–5 annual). Seed/Series A stage warrants monthly granularity early given high burn sensitivity.

- **Key drivers & assumptions:**
  - Number of broker/distributor clients: 3 named at Seed; ramp ~5–8 in Y1, ~15–25 by Y3 (early-stage InsurTech ramp typical)
  - ARR per broker client: disclosed but redacted; €50k–€200k ARR/client range typical for B2B insurance SaaS at this stage - needs client confirmation
  - GWP per vertical/client: varies widely by vertical (rental insurance vs. motor vs. breakage); model should parametrize GWP per client by vertical
  - Loss ratio by vertical: 55–70% for P&C lines (industry benchmark); Seyna reinsures bulk - model net retention ratio assumption needed
  - Reinsurance cession rate: 70–85% of GWP ceded to reinsurers (Swiss Re, Munich Re etc.), leaving net earned premium on balance sheet - confirm with company
  - Net earned premium margin (after cessions): 15–30% of GWP retained
  - SaaS gross margin: 70–80% (software-only component; industry standard B2B SaaS)
  - New vertical launches: +3 verticals at Series A; 6-month ramp per new vertical
  - Geographic expansion: 1 new country at Series A; 12-month ramp, revenue = 20% of France run-rate in Y1 of new market
  - Headcount growth: engineering-heavy team; 20–35 FTEs at Series A raise, scaling to 60–80 by Y3
  - Burn / opex: €300k–€500k/month at Series A stage (tech + compliance + insurance ops)
  - ACPR compliance / regulatory costs: material fixed cost line (~€200–400k/year); model separately

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - Base: 15 broker clients by Y3, 60% GWP cession rate, 3 verticals France + 1 country
  - Bull: 25 clients by Y3, faster vertical ramp, higher ARR/client (€200k+), 2 new countries
  - Bear: Client acquisition slower (8 clients Y3), loss ratio spikes (cat event in a vertical), 1-country-only

- **Required sheets / outputs:**
  1. Assumptions (all inputs, scenario toggle)
  2. Broker/Client Roll (new clients added per period, ARR per client, churn)
  3. GWP Schedule (gross written premium by vertical, by client, net of cessions → net earned premium)
  4. SaaS P&L (ARR, MRR, gross margin)
  5. Insurance P&L (net earned premium, loss ratio, expense ratio, combined ratio, underwriting profit)
  6. Consolidated P&L (SaaS + Insurance P&L merged)
  7. Balance Sheet (insurer-specific: technical reserves, reinsurance receivables, equity)
  8. Cash Flow Statement
  9. Runway / Funding bridge (burn vs. cash, months of runway, next raise trigger)
  10. Dashboard (KPI summary: ARR, GWP, combined ratio, clients, burn, runway)

## Frequently asked questions

### Is the Seyna financial model free?

Yes. The Seyna model is a free Excel download with live formulas.

### Can I change the assumptions?

Yes. The workbook is editable and its live formulas recalculate when assumptions change.
