Telecommunications

Reasoning over network signals

Anomalies hide in the shape of the traffic, and a threshold alert rarely tells you what changed or whether it matters. TSLMs read call-detail signals natively and explain in plain language why a window looks suspicious, so analysts act on a reasoned verdict, not just a flag.

Telecommunications · Example Use Case

Telco Fraud Detection

From threshold alerts to reasoned verdicts.

[01] SENSE & ENCODE

Every call streams live signals: volumes, authentication failures, handle times, reset requests. The TSLM reads them together with device fingerprints, account history and past incident patterns.

[02] REASON & EXPLAIN

When the pattern turns coordinated, the queue speaks up. The analyst asks questions in plain language and the TSLM answers with the likely scheme, backed by the signals and history.

[03] ACT & EXECUTE

The TSLM proposes one concrete containment with the expected outcome. The analyst approves or declines — then step-up verification rolls out automatically across the flagged accounts.

[04] SYSTEM IMPLICATIONS

Before the decision, every option is priced for the whole operation: fraud losses, honest-caller friction and queue load if containment is deferred versus rolled out now.

CALL CENTER · QUEUE NORMAL

CONTROL BOARD

! CALL CENTER · AUTH GATEWAY · SELF-REPORTED · 10:12

Flagging the queue: authentication failures up 6x in 25 minutes, concentrated on 340 accounts, with callers pushing for credential resets. The pattern looks coordinated.

→

THE SEQUENCE PLAYS AUTOMATICALLY · REPLY OR DECIDE ON THE CONTROL BOARD · CLICK A STAGE TO JUMP · ↺ RESET TO REPLAY

HOW IT WORKS

Wholesale telecom fraud costs the industry tens of billions each year, and rule-based systems remain the standard defense. But fixed thresholds miss anomalies that sit just inside the boundary, and when a rule does fire, it offers no explanation, leaving an analyst to inspect the underlying traffic by hand before reaching a verdict.

A Time Series Language Model reads the raw call-detail signals directly: call volumes, durations, unanswered calls, revenue and cost across the recent window. It describes what is unusual in the pattern, distinguishes genuine fraud from atypical but legitimate traffic, relates it to known fraud signatures, and produces a clear Fraud or No-Fraud verdict preceded by the reasoning that supports it.

The verdict and its rationale appear as a second opinion inside the analyst’s existing review interface, next to the rule-based alert, for the analyst to confirm rather than replacing their judgment. Because every verdict comes with an explanation, senior analysts review faster and junior analysts reach sound decisions sooner, turning a manual inspection step into a reviewable, explainable one.

EXEMPLARY DATA SOURCES

CALL DETAIL RECORDSCALL DURATIONSTARTED AND UNANSWERED CALL COUNTSREVENUE AND COST PER SLOTTRAFFIC PROFILESDESTINATION AND ROUTE PATTERNSRULE-BASED ALERT HISTORYKNOWN FRAUD CASE PATTERNS

Telecommunications: the signals a TSLM reads natively

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