Trudenty provides a privacy‑preserving Trust Network that processes transaction and behavioural data inside a client’s secure clean‑room to generate real‑time, explainable trust scores. By combining machine‑learning, behavioural science and network‑level signals, the platform helps payment service providers, acquirers, processors and merchants detect and prevent first‑party fraud while complying with ISO 27001 and SOC 2 Type II security standards.
Funding
$591.3K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OFounders
Product
Problem
First‑party fraud—such as friendly fraud, chargeback abuse, and false claims—costs merchants, PSPs, and acquirers billions annually, but data silos and privacy constraints prevent sharing the intelligence needed to detect it effectively.
Solution
Trudenty offers a privacy‑preserving Trust Network that processes transaction and behavioural data within the client’s secure environment, generating real‑time trust scores for each consumer. Machine‑learning models combine behavioural, transactional, and network‑level signals to differentiate genuine shoppers from fraudsters. The platform delivers explainable risk indicators, including reason codes and fraud probabilities, which can be embedded directly into payment flows, underwriting, and dispute management. All analytics run in a clean‑room architecture, ensuring that raw data never leaves the organization while still enabling collaborative intelligence across merchants, PSPs, and acquirers. The solution is built to meet enterprise security standards (ISO 27001, SOC 2 Type II) and integrates via APIs, webhooks, and SDKs.
Target Audience
Primary customers are payment service providers, acquiring banks/processors, and online merchants seeking to reduce first‑party fraud and improve dispute outcomes.
Features
- Embeddable trust intelligence layer that operates inside the client’s clean‑room, guaranteeing no raw data leaves the organization
- Real‑time Trust Scores with explainable reason codes and fraud probability metrics for transparent decision‑making
- Machine‑learning and behavioural science models that fuse transaction, behavioural, and network signals to detect first‑party abuse
- API, webhook, and SDK integrations for seamless embedding into checkout, pre‑authorisation, refunds, and dispute workflows
- Enterprise‑grade security and compliance (ISO 27001, SOC 2 Type II) with strict privacy controls
- Sandbox environment for testing and a production guide for live deployment