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ThreatMark

ThreatMark is committed to fostering trust in the digital world by safeguarding individuals from fraud and protecting their personal information. Our advanced Behavioral Intelligence solutions enable financial institutions and fintechs to detect and prevent fraud attacks before they occur. By harnessing the power of behavioral data and analyzing user-device interactions, we proactively identify and thwart fraud, providing financial entities with a robust, future-proof defense against evolving scams. Through close collaboration with our partners, we not only help combat fraud but also significantly reduce the false positives generated by existing fraud controls.

  • Vendor typesProducts & Devices
  • Client typesB2B (enterprises)
  • Business ImpactsCustomer Product & Service Innovation, Operational Efficiency & Cost Savings, Risk Reduction & Compliance
  • IndustryFinancial Services, Markets & Insurance, Telecommunications
  • Application areasLegal, Compliance & Risk, Product Development, Innovation & R&D
  • Solutions & Services

    Ready-Made Solutions

    Products & Applications, SaaS

    Research & Innovation

    Research & Experimental Development
  • Technical expertises

    Classical AI & Applications

    AI for Cybersecurity & Threat Detection

    Data & Infrastructure

    Data Engineering & Architecture, Data Governance, Quality & Compliance, Specialized AI Data Platforms & Tools

    Machine Learning & MLOps

    Classical ML & Applied Predictive Modeling

    Research & Innovation

    Advanced Algorithm & Model Development, Experimentation & Evaluation Methods

    Robotics & Embedded AI

    Edge AI / Embedded Systems

fraud detection, scam prevention, behavioral biometrics, transaction risk analysis, digital identity verification, synthetic identity detection, phishing detection, mule account detection, account takeover prevention, social engineering fraud detection, online banking fraud protection, real-time fraud monitoring, anomaly detection, device fingerprinting, session monitoring, machine learning fraud models

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