Ladder Financial Model
InsurTech Startup Financials (Free Excel Download)
Digital-native, full-stack life insurance carrier using proprietary ML underwriting to deliver instant, fully underwritten term life policies online.
professionals from Deloitte
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About this model
Ladder is a digital-native, full-stack term-life insurance carrier with instant underwriting and policies sold entirely online. Its proprietary longevity score uses machine learning to assess risk and it also exposes embedded insurance APIs for partner distribution.
As a carrier, Ladder earns premiums and retains underwriting risk rather than merely taking a broker commission. The company reported 4.5 times growth from 2019 to 2020, a 5.2x LTV-to-CAC ratio, and strong mobile adoption.
The model is life-carrier underwriting economics. Policies, premium, persistency, embedded-channel volume, claims, reinsurance, loss ratio, and acquisition costs determine profit. Underwriting accuracy, capital needs, and channel mix drive returns.
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 Ladder
ladderlife.com
How to build a detailed financial model for Ladder
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Ladder model - distilled from its pitch deck and publicly available information.
Product & value proposition
- 100% digital term life insurance: instant approval, fully underwritten at point of sale.
- Coverage range $100K–$8M, terms 10–30 years.
- Mobile-first application flow: 74% of apps submitted on mobile.
- Proprietary "Ladder Longevity Score": ML model across thousands of data points for real-time underwriting.
- Embedded insurance APIs (Calculator, Quote, Application, Instant Coverage, Admin) enabling partners to integrate life insurance into third-party products.
- Full-stack carrier (not an MGA or fronting arrangement).
Market
- Life insurance is positioned as the largest Insurtech segment - qualitatively dwarfs pet, renter's, homeowner's, and auto insurance. No dollar TAM figure provided.
- No TAM/SAM/SOM numbers in deck.
Revenue model
- Premium revenue: policyholders pay monthly/annual premiums on term life policies. Ladder is a full-stack carrier so it retains underwriting risk and earns net premiums minus claims and reinsurance.
- Embedded insurance channel: API partners distribute Ladder policies; revenue model for this channel not specified (likely ceding/fronting fee or shared premium).
- Policy example shown: $800,000 coverage, policyholder name "Kendrick Cue" - no premium price displayed.
- No pricing (monthly premium rates, average policy size) given in deck.
Traction & metrics
- Y/Y growth: 4.5x (2019→2020); bar chart shows volumes from 2017–2020 but absolute policy or premium figures are not labeled on bars.
- LTV/CAC: 5.2x.
- Customer demographics: 85% of customers under 45 years old; 76% first-time buyers; 74% apps submitted on mobile.
- NPS: 82 (Ladder) vs. industry Life average 26, Netflix 68, Apple 68, Amazon 62, Spotify 54.
- Retention by cohort: Year 1 = 93%, Year 2 = 96%, Year 3 = 97%.
- No revenue, premium volume, policy count, or customer count disclosed.
Unit economics
- LTV/CAC ratio: 5.2x.
- Absolute CAC and LTV values not disclosed.
- Loss ratio, expense ratio, or combined ratio not disclosed.
- Retention cohort data implies very low annual lapse rates (3–7% in years 1–3), which supports a long-duration premium stream.
Competition / moat
- Incumbent life insurance industry NPS = 26; Ladder NPS = 82 - cited as evidence of structural customer experience advantage.
- Proprietary Ladder Longevity Score ML model.
- Data flywheel: more customers → more data → better ML → better underwriting → lower prices / better risk selection → more customers.
- Full-stack carrier status (vs. MGA/fronting) cited as a structural advantage.
- Embedded API distribution reduces customer acquisition cost and diversifies channels.
- Named competitors: not listed. Industry incumbents (traditional carriers) implied as the comparison set via NPS benchmark.
Team & funding ask / use of funds
Recommended financial model
- Archetype + why: Insurance GWP / loss-ratio model (life insurance carrier P&L). Ladder is a full-stack carrier earning net premiums, not an MGA or marketplace. The core model must track Gross Written Premium (GWP) → net earned premium → claims/loss ratio → operating expenses → underwriting income, layered with a policyholder cohort build (new policies issued × average face amount × premium rate). The embedded API channel adds a distribution/partner revenue line. A 3-statement wrap can be added once operating assumptions stabilize.
- Forecast horizon & granularity: 5 years (Year 1–5), annual columns. Monthly granularity for Year 1 only (useful for cash management given carrier capital requirements). Base year anchored at 2020 given that is the last disclosed data point.
- Key drivers & assumptions:
- New policies issued per year
- Average face amount per policy; use as proxy pending confirmation]
- Average annual premium rate
- Policy lapse / churn rate - Year 1: 7%, Year 2: 4%, Year 3: 3% (inverse of retention)
- Loss ratio (claims as % of net earned premium)
- Reinsurance cession rate
- Operating expense ratio
- LTV/CAC = 5.2x; absolute CAC
- NPS = 82 - informational; drives retention assumption
- Y/Y growth 4.5x in 2020
- API/embedded channel as % of new policies
- Scenarios (Base / Bull / Bear - which variables flex):
- Bear: growth decelerates to 1.5x/yr, loss ratio 65%, CAC inflates +30%
- Base: growth 2–3x/yr, loss ratio 55%, CAC stable
- Bull: growth 4x+/yr (API channel scales), loss ratio 48% (ML advantage realized), CAC falls 20% as brand grows
- Required sheets / outputs:
- Assumptions - all drivers with / tags
- Policy Cohort Build - new policies by year, cumulative in-force, lapses
- GWP & Premium P&L - GWP → ceded premium → net earned premium → claims → underwriting margin
- Operating Expense Model - tech, marketing (CAC × new policies), G&A, headcount
- Unit Economics - LTV/CAC bridge, payback period
- Income Statement (3-statement optional)
- Capital / Surplus - carrier statutory capital requirements (RBC ratio)
- Scenario toggle
- Dashboard - KPI summary (in-force policies, GWP, loss ratio, NPS, LTV/CAC)
Frequently asked
Is the Ladder financial model free?+
Yes. The Ladder 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 Ladder'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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