Handset Fraud
Subex scores every POS and e-commerce transaction instantly with ML-based risk models, blocking fraudulent device sales in real time, before losses are written off and before your team opens a single investigation. Fraud is stopped at the point of sale, not chased down weeks later. More devices sold clean, fewer losses to absorb.
Overview
The increasing value of handsets and other devices (smart watches, routers, fixed line phones and other CPEs) has made them a prime target for fraudsters. The Subex handset fraud solution helps you detect and block handset and other device fraud activity across retail POS, tele sales, and other digital channels with real-time machine learning (ML) based risk scoring. Our solution provides extensive fraud coverage via a range of detection techniques to help you avoid major financial and reputational costs associated with the handset fraud.
See How We Can Help Your Organization Combat Handset Fraud
ML models provide recommendations to the point of sale, enabling not just a risk score but also providing alternative recommendations for high/least risk numbers
With the value of handsets ever-increasing, Subex’s solution can help deliver rapid ROI on the controls
Real-time decisions within seconds enabled through AI/ML-based risk scoring improves fraud detection
Identification of account takeovers, subscription fraud and other related fraud methods using real-time fraud rules
The solution integrates feedback from siloed pointed systems (like physical ID verification, credit scoring, the email id/domain risk, IP based risks, device fingerprinting) and returns unified weighted response
AI/ML model is built to decrease false positives significantly
Check Out Handset Fraud Solution Features
Evaluates every incoming order and assigns a risk score based on various features in the order in real-time to help decision making about the order
Monitor fraud with simple threshold-based rules to complex rules and velocity checks. A smart pattern feature helps to monitor the sequence of activities by the fraudsters.
Provide various insights like orders identified as frauds, fraud loss averted, rule performances, etc. to business & fraud managers in near-real-time
Allows to visualize data records in the system across all the streams
The solution integrates easily with external API for credit scoring, device fingerprinting, email Id/domain risks, etc.
- Whitepaper
Combating Handset Fraud
- Point of View
The Need for Telcos to Pay Attention to Handset Fraud
Resource Center
- Point of View
Empowering Telcos to Benefit from Device Sales Opportunities
- Whitepaper
AI Agents in Telecom Fraud Management
- E-book
AI-First Defense Against Telecom Fraud
Frequently Asked Questions
What is handset fraud, and why is it different from other telecom fraud?
Handset fraud is fraud committed during the sale of a mobile device, using a false identity, a compromised account, or a genuine identity with no intention to pay. It’s different from most telecom fraud because the loss happens at the moment of sale, before usage or billing records exist, so traditional fraud tools that rely on post-activation data are structurally too late to catch it.
How is Handset FraudZap™ different from a general fraud management system (FMS)?
Traditional FMS platforms are built to analyze call and usage records generated after activation. Handset FraudZap™ makes its decision during the order itself, using identity, account, device, payment, delivery, and channel signals, so risk is assessed before the device ships, not after the loss has already occurred.
Does it work in real time?
Yes. Handset FraudZap™ scores each transaction instantly at the point of order, across both POS and e-commerce channels, so a decision is available before fulfilment rather than in a later batch cycle.
What data does it need to operate?
It requires a core dataset of transaction data, order and payment details, device identifiers, and basic customer or account information. Additional signals, such as behavioral history, clickstream data, prior account activity, location, and third-party risk indicators, can be added to strengthen detection further, but are not required to get started.
Is Handset FraudZap™ only for handset or device fraud?
Currently, yes, handset and device fraud across POS and e-commerce is the generally available use case. FraudZap™ is built as a platform designed to support additional fraud use cases over time, delivered one focused use case at a time.
How does it reduce false positives for genuine customers?
Handset FraudZap™ is built on a carefully chosen, high-quality minimum data set rather than requiring every possible data point from every customer, and its detection models are tuned specifically for handset transaction patterns rather than adapted from generic retail fraud logic. This combination is designed to catch fraud precisely without unnecessarily declining legitimate orders.
Should we choose Handset FraudZap™, extend our existing FMS, or build a solution in-house?
It depends on how quickly you need protection and how much ongoing engineering and fraud-content ownership you can sustain. Handset FraudZap™ is the fastest route when speed and pre-built handset-specific intelligence matter most; extending an existing FMS makes sense if it already supports order-level data and low-latency decisioning; building in-house makes sense only if full architectural control is a strategic priority and you can commit to maintaining the detection models, rules, and infrastructure long-term.
Can Handset FraudZap™ work alongside our existing fraud management platform?
Yes. It’s designed as a focused, purpose-built layer for handset and device fraud specifically, and is intended to complement, not replace, your broader fraud management stack.
Cloud or on-premises?
Handset FraudZap™ supports all deployment models: on-premises, private cloud, or public cloud.
How long does deployment typically take?
Handset FraudZap™ is built for rapid deployment, going live in days rather than the months typically required for a full platform build-out or extension, since it deploys with pre-built rules and models rather than requiring them to be developed from scratch.