Companies · Fintech
San Francisco · CA, USA · Fintech · founded 2026 · https://www.incandor.com
Diligence memoA one-page analyst read on Incandor — recommendation, valuation, rhythm, risks.→Incandor: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Incandor is one of 1063 Fintech companies tracked from San Francisco, CA, USA, on record since 2026. By capital raised it ranks mid-pack (ahead of 66% of sector peers), and mid-pack by modeled valuation est..
Ranking is computed against this company's own sector cohort — reported capital is fact; valuation tiers are modeled.
AI analyst read est. — model-extracted from this company's public description, not a verified fact. 30%
operates a technology-led product inferred from public copy
Grounded in: “You are a venture analyst”
Behavioral intelligence infrastructure for anti-fraud
Incandor detects fraud on banking and fintech platforms by learning how every user physically behaves — mouse dynamics, keystroke timing, scroll patterns, and on mobile, how they hold their phone. Founded by two Stanford engineers and backed by Y Combinator, Incandor builds a behavioral map of every user on your platform — no fraud labels or historical data required. At the individual level, it identifies account takeovers with >99% accuracy. At the population level, coordinated rings, mule operators, and coerced sessions separate out naturally. Rather than a black-box risk score, fraud teams query the map via a programmable API.
As reported in public records reported — not modeled.
Solid bars are reported offering amounts reported; hatched bars are the modeled post-money valuation est. — both on one shared scale so you can read raise-vs-worth at each round directly. Use the toggles to overlay data labels and the niche-peer / market average value lines.
No round amounts on record to chart.
No staged rounds to sequence.
Round size and date are reported; the stage label is inferred from round size. Valuation is modeled from stage benchmarks. Directional, not a quoted figure.
Not enough modeled valuation points to chart a trajectory.
Benchmarked against 1063 companies in Fintech. Each bar is a median (the middle company, not an average — outliers don't skew it). Two yardsticks: real money raised (reported on Form D) and modeled value (our estimate est.). These are whole-sector medians across all stages, except the per-stage row.
Raised more than 66% of sector peers (real $). Modeled value above 66% of peers (estimate).
Stage is inferred from round size est., not reported on the filing — a round's dollar size maps to a bucket: Pre-Seed <$1.0M · Seed $1.0M–$4.0M · Series A $4.0M–$15M · Series B $15M–$40M · Series C $40M–$100M · Series D+ $100M–$400M · Growth/Late >$400M.
| Stage | Amount · real | Announced | Post-money · est | Value · est | Conf. |
|---|---|---|---|---|---|
| No rounds recorded. | |||||
Predictive signals are modeled est. from this company's own cadence and step-up, plus sector benchmarks — directional, not advice. Peer set and a CSV export live in your analyst workspace.
Incandor is an official record sourced from the U.S. Securities and Exchange Commission (SEC). U.S. data is aggregated from SEC Form D filings.
Nearest neighbours across the whole database — matched on sector, stage and capital scale, and on shared operators (officers or directors named at both companies in public filings). A discovery shortlist, not a valuation cohort — verify before acting, the same way modeled figures are directional.
| Company | Sector | Stage | Raised · real | Value · est | Why similar |
|---|---|---|---|---|---|
| 1stCollab | Fintech | — | — | — | same sector |
| Abacus | Fintech | — | — | — | same sector |
| Absa Bank | Fintech | — | — | — | same sector |
| Accend | Fintech | — | — | — | same sector |
| Accept.inc (formerly BoardRE) | Fintech | — | — | — | same sector |
| Aer | Fintech | — | — | — | same sector |
| Affinity | Fintech | — | — | — | same sector |
| Ajaib | Fintech | — | — | — | same sector |
Matched by meaning, not labels — a local language model reads each company's name, sector and description and ranks the closest in that learned space. This catches look-alikes that cross sector boundaries; the structured list above explains its matches, this one trusts the text. Directional, like every modeled signal here.
| Company | Sector | Stage | Value · est | Match |
|---|---|---|---|---|
| Finic The AI fraud hunter | AI / ML | — | — | 75% |
| Fintelite Intelligent Process Automation for Lending. | Fintech | — | — | 75% |
| Panelytic Payment infrastructure for machines | Fintech | — | — | 74% |
| Inscribe AI Document Fraud Detection for Banks, Credit Unions & Lenders | Fintech | — | — | 74% |
| Fraud Protection Network, Inc. | Other Technology | Seed | $2.8M | 73% |
| Fraud.Net Inc | Other Technology | Seed | $12.6M | 73% |
| Flagright AI-native AML compliance platform for fintechs & banks | Fintech | — | — | 73% |
| Dojah Inc Prevent Fraud and Onboard Users Faster | Regtech | — | — | 73% |
See where Incandor sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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