Subex's shift: from reactive assurance to predictive decisions
Telecom margins are under pressure, fraud is getting smarter, and 5G introduces complexity most dashboards can't keep up with. That's the backdrop for Subex's push into AI-native operations under CEO Nisha Dutt, with a clear mission: move operators from after-the-fact detection to decisions made in real time.
The core idea is simple. If your assurance function flags issues hours or days later, value is already gone. The future is continuous sensing, instant risk scoring, and automated action with controls your board can trust.
Why reactive assurance falls short
Traditional assurance is built on batch data, static rules, and manual queues. By the time an analyst sees a ticket, the leakage has scaled and the customer impact has spread.
Executives need a system that spots anomalies as they form, tests the likelihood of loss, and decides the next best action without waiting on people for routine cases.
What real-time predictive looks like
- Streaming data across charging, policy, network, partner settlements, and care.
- Entity-resolution to link identities, devices, accounts, and traffic flows.
- Hybrid analytics: rules for known patterns, machine learning for unknowns.
- Closed-loop actions: throttle, block, re-rate, or alert with audit trails.
This is the move from "find and fix" to "sense and decide." It reduces loss, shortens time-to-block, and frees people to handle the edge cases that truly need judgment.
Hypersense: the AI-native engine
Subex's flagship product, Hypersense, is the platform carrying this shift. It brings together real-time ingestion, feature stores, model governance, and decisioning so teams can build, deploy, and monitor AI at production scale.
Key outcomes: lower false positives through better context, faster detection through streaming analytics, and consistent actions through policy-driven automation. The value compounds as more use cases share the same data and models.
Use cases executives care about
- Fraud management: Detect IRSF, SIM box, subscription fraud, and account takeovers using graph analytics, anomaly detection, and velocity checks. Reduce alert noise and cut detection-to-block times.
- Revenue assurance: Monitor order-to-cash in real time; reconcile CDRs, charging, mediation, and billing; catch rating and discount leaks as they happen-especially across 5G, IoT, and partner bundles.
- Margin assurance: Blend network cost, partner fees, and product pricing to protect unit economics. Flag unprofitable routes or segments instantly.
- Experience protection: Proactively spot quality issues that trigger churn and automate corrective actions before customers feel them.
From assisted to autonomous AI: practical levels
- Level 0: Manual reviews, static rules.
- Level 1: Alerts with recommended actions.
- Level 2: Auto-actions on low-risk cases with human oversight.
- Level 3: Self-learning policies with guardrails and audit.
- Level 4: End-to-end autonomic loops across domains with continuous model risk checks.
The target isn't "no humans." It's smart allocation: automation for routine, human judgment for exceptions, and governance across all of it.
What makes this feasible now
- Event-driven architectures and cheaper streaming compute.
- Feature stores to reuse data signals across use cases.
- Model observability to track drift, bias, and performance in production.
- Policy engines that translate risk thresholds into consistent actions.
As fraud patterns and monetization models evolve, these foundations keep the system agile without constant rework.
Executive playbook: 90-day path to momentum
- Pick two pays-for-itself use cases: one in fraud, one in revenue leakage.
- Instrument the data spine: high-fidelity event streams, identity graphs, and quality checks.
- Define guardrails: which actions can auto-execute, when to require human approval, and audit standards.
- Pilot with live traffic: measure detection latency, false positive rate, recovered revenue, and customer impact.
- Close the loop: codify actions in a policy engine; move from recommendations to auto-actions where safe.
- Scale by template: standardize features, models, and policies so each new use case launches faster.
Metrics boards should track
- Detection-to-action time (seconds/minutes).
- Alert precision and false positive rates.
- Revenue leakage rate and recoveries realized.
- Automation coverage (% of cases handled end-to-end).
- Model freshness, drift, and stability across segments.
- Customer impact: blocked-good vs. blocked-bad ratios.
Risk, control, and trust
AI without controls creates new exposure. Hypersense's focus on governance-versioned models, explainability, and auditable actions-helps satisfy compliance and internal audit.
For context on industry priorities in fraud and security, see GSMA's work on telecom fraud and threat intelligence. GSMA Fraud and Security Group
If 5G monetization is on your roadmap, align assurance with dynamic offers and slicing. A good primer on slicing fundamentals is here: ITU: 5G network slicing
Bottom line
Nisha Dutt's strategy is clear: make assurance predictive, make actions timely, and make governance non-negotiable. Hypersense is the operating layer that turns that strategy into day-to-day decisions that protect revenue and improve experience.
If you're building leadership skills to steer programs like this, explore focused training for executives: AI courses by job role and a practical AI automation certification.
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