Problem

Banks typically verify identity when an account is opened and monitor transactions afterward, but lack a continuous, identity-level signal for who is actually operating the account in between. Incandor frames the resulting gap as increasingly costly because attackers can buy verified identities, rotate devices, and run many accounts under different names, while coordinated fraud rings may share no obvious transactions, devices, or credentials. The company describes fraud and money laundering as a multi-hundred-billion-dollar problem; account-takeover fraud alone cost U.S. adults approximately $15.6 billion in 2024.

The central use case is identifying an account takeover or mule handoff—the moment control of an account changes from one person to another—and then tracing that operator across the platform. This can expose a mule operator running numerous accounts, coordinated fraud rings, or a compromised account before funds move or the damage becomes irreversible.

Product / Service

Incandor is a behavioral-intelligence API that analyzes how users physically interact with websites and apps, including mouse movement, keystroke timing, taps, clicks, scrolling, touch behavior, and mobile-device handling. Each session becomes a behavioral point in a population-wide map; the company says each human forms a distinctive cluster, allowing banks to reconcile the real operator across accounts, devices, and credentials without relying on fraud labels, historical data, or an enrollment period.

Rather than returning only a black-box risk score, Incandor exposes the behavioral map through a programmable API that fraud teams can combine with transaction history, customer records, device data, and internal rules. The resulting system is designed to detect operator changes, shared operators, organized rings, coerced sessions, bots, and previously identified bad actors, while collecting behavioral signals rather than what users type, read, or view. The benefit is a continuous session-level signal that works from the first interaction and can support investigation, onboarding blocks, and earlier intervention.

Market

Incandor competes in behavioral biometrics and behavioral-intelligence infrastructure for fraud, abuse, account-takeover, mule-account, and financial-crime detection. Its direct and adjacent competitors include BioCatch, NeuroID, Sardine, and LexisNexis ThreatMetrix with BehavioSec. These companies overlap in using behavioral, device, network, or telemetry signals to detect fraud; BioCatch is the most visibly established comparable, reporting protection for more than 350 retail banks and $4 billion in fraudulent transactions prevented in 2025.

Incandor is an early-stage YC-backed company in the Spring 2026 batch, founded by two Stanford engineers and listed as active in San Francisco. Its public materials say it is onboarding early customers across commercial and consumer banks, fintechs, neobanks, and digital banks, but disclose no named customers, revenue, or production-scale deployment. The strongest supported characterization is therefore an early, likely pre-revenue or pilot-stage startup rather than a scaled commercial vendor; zero revenue cannot be confirmed from the available public evidence.