Telecom fraud detection

Catch the fraud your BSS can't.

Real-time detection of SIM box, Wangiri, IRSF and roaming fraud. Signalling and mediation feeds scored as events arrive, correlated with your rated CDRs on a Kafka-compatible streaming core. Deployed in weeks.

  • Sub-150ms decisions, end-to-end
  • Built for operators that need Tier-1 fraud capability without building a Tier-1 data engineering team.
  • Belgian-incorporated, GDPR-aligned
<150ms
end-to-end
Every CDR scored by every active rule, in parallel to the call flow
Unified
fraud model
SIM box, Wangiri, IRSF, roaming, high-usage, and SMS
Passive
by default
Never inline. If Tellaris stops, every call still completes
2-3 wk
to deployment
Customer-isolated passive-mode. Half a day to first alert on a CDR replay
The problem

Mid-size operators lose 2-5% of revenue to fraud every year. Enterprise fraud suites are often too slow, expensive, or heavy for them.

Tier-1 carriers run Subex, Mobileum, or custom-built fraud stacks staffed by 20-person teams. If you're an operator serving 1-20M subscribers, the licensing alone prices you out. Meanwhile SIM box bypass is draining your international termination revenue, Wangiri bots hit your subscribers overnight, and IRSF quietly inflates wholesale invoices you pay 60 days later.

  • Revenue leakage is invisible until the wholesale invoice lands. By then the ROI to contest is marginal and the fraud ring has moved on.
  • Static rules miss adaptive fraud by design. Attackers now rotate routes, slow their rates, and mimic normal usage to stay under thresholds tuned for yesterday's threat model.
  • "AI fraud detection" from big vendors ships in 18 months. You need something in production this quarter, not after the next board review.
The platform

One pipeline from stream ingestion to enforcement.

  1. 01
    01 · Ingest

    Stream every CDR, signal, and subscriber event

    Kafka-compatible ingestion from your mediation layer, rated-CDR exporters, and signalling probes. Flexible ingest adapters with governed Avro contracts once feeds are onboarded, so we adapt to your data rather than the other way around.

    Tier-2/3 CDR rates, load-tested beyond
  2. 02
    02 · Detect

    Correlate across streams in real time

    Our stateful core joins voice, signalling, and subscriber-profile signals in a single pass. Rule engines catch known fraud patterns, ML scoring surfaces the novel ones.

    Sub-150ms end-to-end
  3. 03
    03 · Act

    Automated response. Audited handoff.

    Trigger a block, rate limit, step-up review, network-policy action or case-management workflow. Every decision is logged with the signals that triggered it, so analysts can see why.

    Explainable by default
Deployment posture

We complement your fraud stack. We don't replace it.

Tellaris is a real-time decision layer that runs in parallel to the call flow, marrying the signalling layer with the commercial reality of your rated CDRs. It's the glue between systems that don't talk to each other, not another rip-and-replace.

Adds value to the operator's existing fraud system. We don't replace it. We don't touch the call flow.

Passive by default

We consume parallel feeds, never inline. Rated and mediation CDRs today; GGSN / PGW, Diameter on the DRA, and SS7/SIGTRAN light up as you publish them. If Tellaris stops, every call still completes.

Sits alongside your fraud stack

Tellaris runs next to Subex, Mobileum, clearing-house feeds, and in-house tooling. It correlates the signalling layer with the commercial reality of your CDRs, the silos those systems can't see across.

Operator-controlled enforcement

Decisions map to your own BSS endpoints, ALLOW / TIGHTEN_RULES / CAP_SPEND / BLOCK, via the Triggers page. Fail-closed allowlist, bearer or HMAC auth, no surprise destinations.

Active mode is a later step

Inline enforcement is an upgrade once trust is established, never the starting position. You decide if and when to move from monitoring to action.

Fraud coverage

Fraud typologies. One unified model.

Because these typologies share substrate features (velocity, fan-out, destination risk, subscriber deltas), a single streaming pipeline covers them all and extends to new variants as they emerge, alongside your existing systems and not one project per fraud type.

SIM-BOX

SIM box bypass

International voice traffic re-terminated over GSM gateways to evade settlement. We detect by correlating call patterns with HLR location, velocity, and CDR signatures.

Primary signal Velocity + location + traffic-pattern anomaly
WANGIRI

Wangiri callback fraud

Missed-call bots seeding callbacks to premium-rate numbers. We catch the campaign footprint in seconds, not after complaints land at customer care.

Primary signal Fan-out pattern + premium-rate destination
IRSF

International revenue-share fraud

PBX compromise → high-value calls to IPRN destinations. We rate-limit and alert before the first full-hour session completes.

Primary signal Destination risk + unusual session duration
ROAMING

Roaming fraud

Impossible-velocity country flips and high-cost roaming abuse. We correlate location, velocity, and destination risk to catch it before the roaming invoice lands.

Primary signal Velocity + location + destination risk
HIGH-USAGE

High-usage abuse

Runaway consumption from PBX hijack, compromised SIMs, and tariff arbitrage. We cap spend before the loss compounds, rather than after the bill is cut.

Primary signal Usage spike + spend velocity
SMS-SPAM

SMS spam & smishing

Phishing campaigns and A2P grey-route abuse. Metadata-first detection on originator reputation and velocity, with optional content classification where legally enabled, flags new campaigns within minutes.

Primary signal Originator + velocity + optional content
Architecture

Boring, proven infrastructure. No vendor lock-in.

Every layer of Tellaris is open-source or open-standard. The streaming fraud-detection blueprint (Kafka-compatible ingest, stateful operators, ML scoring) is a proven carrier-grade pattern. Tellaris productizes it: deployable in weeks for operators who don't have a 20-person streaming team.

Layer Technology Note
Orchestration Helm on k3s or vanilla Kubernetes Self-host or managed
Detection engine Stateful in-process decisioning · rules + ML scoring Sub-150ms end-to-end
Ingestion Kafka-compatible streaming · Avro schemas + registry Backward-compatible CDR contracts
Enrichment PostgreSQL + in-memory cache Subscriber + destination risk features
Observability Prometheus · Grafana · OpenTelemetry Per-rule SLO dashboards
Why the approach works

Grounded in proven detection, not promises.

Tellaris productises a detection approach with a track record in research and at carrier scale, on infrastructure operators already trust. The evidence, not a pitch.

FAME research project
<5% false positives

Independent benchmark for ML-based international bypass detection. An industry reference, not a Tellaris result, and the accuracy bar our scoring is built to clear.

Real-time by design
Real-time continuous, not batch

Every event is scored and acted on as it streams in, not surfaced in tomorrow's batch report. That is the gap static, after-the-fact tooling leaves open.

CFCA industry estimate
~$40B lost per year

The scale of the problem, climbing yearly while prevention spend stays structurally underfunded. The case for getting ahead of it, not chasing it.

Security & compliance

Built to pass your procurement review.

Belgian-incorporated. No third-party data brokers. Every decision is logged for analysis, audit and compliance review.

  • GDPR-aligned
    Pseudonymised at the ingest edge (HMAC-SHA256)
  • Deploys in your infrastructure
    No SaaS dependency at runtime
  • ISO/IEC 27001:2022
    12-month certification track
  • Hardened supply chain
    Signed images, SBOMs, CI-gated scanners
Next step

See Tellaris running on a replay of your CDRs.

Bring one hour of anonymized CDRs to the demo call. We'll show you the fraud patterns our pipeline finds, which rules trigger, and which ML scores surface the non-obvious cases. 30 minutes, no slides.