Turn fraud detection from
reactive alerts into real-time,
intelligent prevention.

Key capabilities Use cases Why us

Fraud is no longer rule-based. It is behavioral, adaptive, and increasingly human-driven.

Yet most fraud detection systems still rely on static rules, delayed reviews, and fragmented signals. The result is familiar high false positives, slow response, increased operational cost, poor customer experience. Visibility exists. Prevention does not.

So we built FRM, an AI-driven Fraud Risk Management platform designed to detect, prioritise, and respond to fraud as it happens, not after damage is done. Our service is delivered via the AI SecOps platform platform and combines AI, machine learning, LLMs, and behavioral intelligence to continuously analyse transactions, user behavior, sentiment, and context, and take action in real time

This is not fraud monitoring. This is fraud intelligence and prevention.

Key capabilities

AI-Driven Fraud Detection & Behavioral Intelligence

  • Behavioral biometrics capturing typing speed, mouse movement, and mobile gestures
  • Transaction pattern analysis across financial and non-financial events
  • Anomaly detection for unusual logins, device usage, IP behavior, and access patterns

Real-Time Risk Scoring & Decision Engine

  • Real-time scoring of transactions and user actions
  • Dynamic risk categorisation (red / green tagging)
  • Context-aware decisioning aligned to business rules and policies

Sentiment Analysis & Communication Intelligence

  • AI-driven sentiment analysis of customer interactions
  • Detection of stress, coercion, or suspicious communication patterns
  • Enrichment of fraud cases with conversational context

Automated Workflows, Escalation & Reporting

  • Automated alerts, notifications, and approval workflows
  • Smart routing of high-risk cases for human review
  • Automated reporting and audit-ready fraud summaries

LLM-Based Analyst & Investigation Interface

  • Inventory of algorithms, keys, certificates, and cipher suites
  • Quantum Impact Scoring to guide PQC migration
  • Prioritised crypto remediation based on asset criticality

Seamless Integration with Banking & Enterprise Systems

  • Integration with core banking systems, transaction switches,
    payment gateways
  • Works alongside existing fraud detection and behavioral analytics tools
  • Parallel deployment with gradual transition to AI-led operations

Use cases

Real-Time Transaction Fraud Detection

Identify and stop fraudulent
transactions before completion.

Insider & Account Takeover Detection

Detect anomalous behavior
indicative of misuse or compromise.

False Positive Reduction

Improve customer experience by
reducing unnecessary blocks and
reviews.

Fraud Operations Automation

Accelerate investigations, approvals,
and reporting with  AI-driven
workflows.

Regulatory & Audit-Ready Fraud Governance

Maintain traceability, reporting,
and compliance across fraud cases.

Why us

AI-native, not rule-driven

FRM adapts continuously using AI, ML, and LLMs, not static thresholds.

Part of a unified security & risk fabric

FRM integrates seamlessly with SIEM++, RBVM, Unified BOM, Resiliency Operations, Observability, and GRC.

Reduces cost without increasing risk

Lower false positives, faster response, better outcomes.

Built for BFSI and regulated environments

Designed with security, compliance, and
auditability at the core.

Behavioral + transactional

intelligence combined We correlate how users behave with what they transact.

Question Mark

Get in Touch

Have a question? We'd love to hear from you. Send us a message and we'll respond as soon as possible.

    contact@istreetnetwork.com