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Iotron AI

Iotron Guard · Live · Fraud, anomaly & risk detection

Static rules miss new fraud patterns and flood analysts with false alerts. Teams need fast decisions they can explain to customers, auditors and regulators.

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See how it works
Iotron Guard interface
Illustrative interface of Iotron Guard, with example data; not a live system or customer data.

01How it works

Data in, intelligence applied, action out.

Iotron Guard interface
Illustrative interface of Iotron Guard, with example data; not a live system or customer data.
  1. 01

    Stream

    Send events (payments, logins, calls, logs) through an API or message stream.

  2. 02

    Score

    Rules and models produce a risk score and reason codes within milliseconds.

  3. 03

    Decide and learn

    Automate low-risk decisions, queue the rest for analysts, and feed outcomes back to improve models.

02Capabilities

What it does.

Configured to your assets, documents or systems — and integrated with the tools your teams already use.

  1. 01

    Real-time scoring API and batch scoring

  2. 02

    Hybrid rules-plus-model decisioning with versioning

  3. 03

    Reason codes and feature contributions for every score

  4. 04

    Case queue, notes and outcome labelling for analysts

  5. 05

    Entity and device linkage to spot rings and repeat abuse

  6. 06

    Threshold simulation before changes go live

AI inside

  • Gradient-boosted models for supervised fraud scoring where labels exist
  • Unsupervised and semi-supervised anomaly detection for new patterns
  • Graph features to surface linked accounts and devices
  • Drift monitoring and scheduled retraining with champion/challenger evaluation

03Architecture & deployment

Built to fit what you run.

  1. 01Event stream
  2. 02feature computation
  3. 03rules + model ensemble
  4. 04risk score and reasons
  5. 05decision API
  6. 06case management
  7. 07feedback labels.
Deployment
  • Cloud (SaaS)
  • Private cloud
  • On-premises; low-latency deployment near your transaction path
Integrations
  • REST and streaming APIs
  • Kafka-style event buses
  • Core banking/payment platforms
  • SIEM and ticketing tools
  • Data warehouses
Security
  • Segregated environments
  • Role-based access
  • Full decision audit trail
  • Encryption
  • Configurable data retention and masking

04Business benefits

Why teams use it.

  • Earlier detection of new fraud patterns
  • Fewer false alerts for analysts
  • Decisions that can be explained and audited

Use cases by industry

  • Fintech and banking

    Payment and account-takeover scoring

  • Telecom

    Subscription and SIM fraud, abnormal usage

  • Cybersecurity

    Anomalous user and entity behaviour

  • Media platforms

    Fake accounts and promotion abuse

Iotron Guard: common questions

Can it work without labelled fraud data?

Yes, anomaly detection can start unsupervised; supervised models are added as outcomes accumulate.

Can we explain a decision?

Every score includes reason codes and the features that contributed.

Does it replace our rules engine?

Usually it complements it; rules and models run together.

Get in touch

See Iotron Guard working on your data.

Tell us about the problem behind your interest in Iotron Guard. We will set up a demo with an engineer who knows the product and talk through deployment, integration and pricing.