# Neuro-ID Financial Model Behavioral analytics SaaS platform that translates "digital body language" into real-time fraud detection and friction reduction scores for digital onboarding. - Canonical: https://finamodel.com/startups/neuro-id - Excel download: https://finamodel.com/startup-models/neuro-id.xlsx - Category: InsurTech - Model type: SaaS ARR / Valuation - Funding round: Series A - Funding: $7M - Founded: 2020 - Geography: US-focused (inferred from customer verticals); global ambition implied by "for the World" franding. - Customer: B2B2C ## About the company Neuro-ID uses behavioral analytics to translate typing, tapping, scrolling, and other digital-body-language signals into fraud and friction scores. A lightweight script helps payments, lending, insurance, and fintech customers identify genuine users and riskier onboarding attempts. The company monitors more than 92 million sessions and reports fraud reduction above 35% for customers. Its commercial model is enterprise subscription software with multi-year contracts and higher ACV on renewal. The model is enterprise SaaS ARR. Customers, contract value, monitored sessions, expansion, renewals, and churn build revenue. Data processing cost, sales capacity, fraud-performance proof, and retention determine margin. ## 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 - Captures digital body language signals (typing, mousing, tapping, scrolling) via a lightweight JavaScript snippet embedded in digital properties. - Outputs: Friction Index® Dashboard, Neuro Attributes for Fraud, Neuro Confidence Scores (NCS), API. - Dual value: (1) fast-track genuine applicants through low-friction journey; (2) flag fraudulent applicants for higher-friction verification. - Use cases on genuine applicants: Fast Track, Friction Index optimization, drop-off prediction, drop-off reduction. - Use cases on fraudulent applicants: low familiarity detection, session fluency, computer savviness, criminal behavior profiling. - Two foundational patents held by academic co-founders. ## Market - TAM framing: "$1.3T Annual Digital Transformation Problem is Growing." - stated as problem size, not an addressable market breakdown. - No SAM, SOM, or formal TAM/SAM/SOM split in deck. - Market narrative: digital transformation has accelerated fraud and friction simultaneously; Neuro-ID positions at the intersection. ## Revenue model - Enterprise SaaS subscription model (inferred from "multi-year contracts & higher ACV" language). - No pricing tiers, ACV ranges, or seat/usage-based pricing disclosed in deck. - Channel: direct enterprise sales (implied by customer description: industry leaders in Payments, Lending, Fintech, Insurance). - Contract structure: multi-year renewals at higher ACV noted as a positive indicator. ## Traction & metrics - 92M+ Sessions Monitored - tracked on a growth chart from June 2017 to June 2020; Y-axis runs 20M–100M; curve starts near zero in mid-2017 and reaches ~92M+ by June 2020. - Reducing Fraud by 35%+ for customers. - Customers: "Industry leaders in Payments, Lending, Fintech and Insurance" - no customer count or named logos disclosed. - Large enterprise customers renewing for multi-year contracts at higher ACV - directional NRR signal only, no number. - CEO Jack Alton: three consecutive liquidity events exceeding $500M (prior track record, not Neuro-ID revenue). - Co-founders: 30,000+ academic citations in HCI. ## Competition / moat - Moat claimed: proprietary patented technology (two foundational patents); unique behavioral data source not available elsewhere. - No competitive landscape slide or named competitors in deck. - Positioning: "Category Leader: Digital Onboarding." - Differentiator: behavioral signal layer is additive to existing AI/ML fraud models (not a replacement). ## Team & funding ask / use of funds - Jack Alton - CEO; go-to-market specialist for disruptive tech; 3 prior liquidity events >$500M. - Joe Valacich, PhD - CSO; co-founder; doctoral professor; 30,000+ citations; HCI global leader. - Jeff Jenkins, PhD - CTO; co-founder; doctoral professor; HCI global leader. - Jon Fetveit - CFO, Head of Strategy. - Courtney Laabs - COO, Head of Customer Success. - Advisors: David Montague (Expedia e-commerce fraud/risk), Brad Pennington (CRO - NS8, Prosper), Brian Elkins (CTO - SmarterHQ), Dave Boyce (Forrester board; CSO - XANT), Forrest Hobbs (CRO - Telesign, Usermind, Cloudleaf), Kevin Moss (former CRO - SoFi; former EVP/CRO - Wells Fargo). --- ## Recommended financial model - **Archetype + why:** Enterprise SaaS ARR model. Revenue is subscription-based (multi-year contracts, expanding ACV), sold to a defined enterprise vertical set. Standard SaaS ARR build (new ARR, expansion ARR, churn, net revenue retention) is the right frame. - **Forecast horizon & granularity:** 5-year annual model (2020–2025), with Year 1–2 monthly detail to capture ramp dynamics. Quarterly suffices for years 3–5. - **Key drivers & assumptions:** | Driver | Value / Source | | -- | -- | | Starting ARR (2020) | ~$1–3M; consistent with early enterprise stage, 92M sessions but no revenue disclosed - treat as placeholder pending DD | | New logo adds per year | 5–15 new enterprise logos/year in early years; industry leaders implies low volume, high ACV | | Average ACV (new) | $150K–$300K; typical for behavioral analytics middleware sold to enterprise fintechs | | ACV expansion rate | 120–130% NRR; deck signals multi-year renewals at higher ACV with no churn signals | | Gross churn rate | 5–10% annually; enterprise cohort, sticky integration (JS snippet embedded in production flows) | | Gross margin | 70–80%; SaaS with lightweight infra (JS + real-time analytics API); no COGS detail in deck | | Sessions growth | 92M+ sessions by June 2020; session volume is a usage/capacity metric, not directly tied to pricing | | S&M as % of revenue | 40–60% in early years; direct enterprise sales motion with specialist reps | | R&D as % of revenue | 25–35%; patented tech requires ongoing development | | G&A as % of revenue | 10–15% | | Fraud reduction delivered | 35%+ for customers - used as a sales proof point, not a financial driver directly | | Pricing model | Annual subscription; likely per-application-session or flat enterprise license - no pricing detail in deck | - **Scenarios (Base / Bull / Bear - which variables flex):** - **Base:** 10 new logos/year, $200K average ACV, 120% NRR, 75% gross margin. - **Bull:** 20 new logos/year (category leadership accelerates), $300K ACV, 130% NRR - driven by expansion into insurance/payments. - **Bear:** 5 new logos/year, $150K ACV, 110% NRR - longer enterprise sales cycles, tighter fraud budgets. - **Required sheets / outputs:** 1. Assumptions dashboard (all drivers, toggle for scenarios) 2. ARR bridge (beginning ARR → new → expansion → churn → ending ARR) 3. P&L (Revenue, COGS → Gross Profit, S&M, R&D, G&A → EBITDA → Net Income) 4. Headcount plan (by function: sales, CS, engineering, G&A) 5. Cash & runway (burn rate, capital required) 6. Session volume tracker (92M base; project forward as a secondary KPI alongside ARR) 7. NRR / cohort analysis (logo cohorts by year; expansion vs. churn waterfall) 8. KPI summary (ARR, NRR, logo count, ACV, CAC, LTV - CAC and LTV estimated from headcount/ACV assumptions) ## Frequently asked questions ### Is the Neuro-ID financial model free? Yes. The Neuro-ID 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.