zkailabs provides a financial behavior intelligence platform that analyzes on-chain wallet activity to generate real-time predictions for fraud, credit, and trading decisions. Its production layer, ❜embed, offers embeddable APIs that rank and present the next best action per user while allowing product teams to set objectives and guardrails without needing ML expertise. The system is designed to reduce fraud losses, improve loan approval rates, and increase trading volume per incentive dollar.
Funding
Funding not disclosed
Founders
Product
Problem
Financial institutions struggle to make real-time decisions on blockchain-based transactions due to a lack of actionable behavioral intelligence. Traditional fraud detection and credit scoring models are often slow, generate excessive false alerts, and fail to capture the nuanced risk signals embedded in on-chain wallet activity, leading to higher losses and missed opportunities.
Solution
zkailabs provides a financial behavior intelligence platform that analyzes blockchain wallet data to generate real-time predictions for fraud, credit, and trading decisions. The platform's production layer, ❜embed, is a set of APIs that embed directly into a partner's interface and decide, per user and per request, what that person should see next—whether it's a market, token, trade, or post. The behavioral model handles the ranking, while product teams retain control by setting objectives, guardrails, and ranking policies through a console. This approach eliminates the need for an ML backlog, model training, or dedicated data science hires, delivering intelligence as a simple API call that improves product outcomes.
Target Audience
Primary customers are financial institutions, fintech companies, and trading platforms that need to understand on-chain behavior to improve fraud detection, credit decisions, and user engagement.
Features
- Real-time fraud scoring for blockchain wallets, enabling lower losses at the same approval rate with fewer false alerts
- Credit loan and counterparty prediction models that approve more borrowers at the same loss rate
- Next trade prediction capabilities that increase incremental trading volume per incentive dollar
- Payment prediction features that accelerate identification of suspicious activity and improve unit economics
- Embeddable API layer (❜embed) that integrates directly into partner interfaces for per-user, per-request decisioning
- Console-based control for setting objectives, guardrails, and ranking policies without requiring ML expertise