Malipo AI provides a real‑time contextual detection platform that uses deep‑learning models and domain‑specific risk analytics to identify AI‑generated fraud such as deepfake KYC videos, synthetic voice impersonations, and tokenized settlement attacks. The system ingests transaction, identity, and behavioral data, delivering instant alerts via APIs and SDKs for banks and fintechs to prevent losses and maintain compliance.
Funding
Funding not disclosed
Founders
Product
Problem
Financial institutions face a growing wave of AI-powered fraud, including deepfake avatars, synthetic voice impersonations, AI-generated identity documents, and automated attacks on tokenized deposit settlements. Traditional rules‑based detection systems cannot keep pace with the speed and adaptability of these machine‑driven schemes, leading to increased fraud losses and compromised customer trust.
Solution
Malipo AI offers a contextual detection platform that leverages advanced machine‑learning models combined with domain‑specific risk analytics to identify and block AI‑enabled fraud in real time. The system ingests transaction, identity, and behavioral data, applying dynamic, AI‑driven scoring to spot anomalies such as deepfake video KYC submissions, synthetic voice call patterns, and suspicious tokenized settlement activity. By continuously learning from emerging attack vectors, the platform adapts its detection logic without relying on static rule sets. Alerts are delivered instantly to fraud teams, enabling rapid response and prevention of loss. Integration points include APIs for core banking, fintech platforms, and settlement engines, allowing seamless embedding of AI‑based defenses into existing workflows.
Target Audience
Primary customers are banks, digital‑only banks, and fintech companies that process high volumes of KYC onboarding, voice‑based authentication, and tokenized financial transactions.
Features
- Real‑time analysis of video, audio, and document inputs using deep‑learning models to detect deepfake avatars, synthetic voice calls, and AI‑generated IDs
- Transaction monitoring engine that identifies algorithmic structuring, bot‑driven mule networks, and tokenized deposit settlement exploits
- Contextual risk scoring that combines AI‑derived signals with institution‑specific fraud rules for adaptive detection
- Continuous model retraining pipeline that incorporates new threat intelligence to stay ahead of evolving AI fraud tactics
- API and SDK integrations for core banking systems, fintech applications, and settlement platforms, enabling low‑latency deployment
- Dashboard with actionable alerts, investigation workflow tools, and audit logs for compliance reporting