Handset Fraud

Protect your handset sales revenues with real-time ML based risk scoring
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
Enhanced Fraud Detection
Real-time decisions within seconds enabled through AI/ML-based risk scoring improves fraud detection
Increased Accuracy
AI/ML model is built to decrease false positives significantly
Improved Customer Experience
Identification of account takeovers, subscription fraud and other related fraud methods using real-time fraud rules.
Unified Solution
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
See how we can help your organization combat Handset Fraud
Enhanced Fraud Detection
Real-time decisions within seconds enabled through AI/ML-based risk scoring improves fraud detection
Increased Accuracy
AI/ML model is built to decrease false positives significantly
Improved Customer Experience
Identification of account takeovers, subscription fraud and other related fraud methods using real-time fraud rules.
Unified Solution
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

Check out Handset Fraud solution features

AI/ML-based risk Scoring
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
Threshold-based rules
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.
Policy Compliance
Monitors the orders requests to ensure they comply with policies defined
Enhanced Link Analysis and Visualization
Allows to visualize data records in the system across all the streams
Business insights Dashboards
Provide various insights like orders identified as frauds, fraud loss averted, rule performances, etc. to business & fraud managers in near-real-time
Integrate easily with external API
The solution integrates easily with external API for credit scoring, device fingerprinting, email Id/domain risks, etc.
Resource center
AI based Risk Intelligence
Flyer

One stop solution to combat all types of fraud

Combating Handset Fraud
Whitepaper

The whitepaper focuses on the sale of handsets/devices, fraud associated with handset sales, and the must-have capabilities in an anti-fraud solution.

Leading Middle East Operator chooses Subex for a holistic risk management approach
Case Study

Leading Middle East Operator chooses Subex for a holistic risk management approach

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Frequently Asked Question

Some of the fraud methods used by fraudsters to commit device/handset fraud, especially for digital or web-based orders.

  • - Subscription fraud by identity theft where the real identity of a person is obtained through phishing, smishing, etc., and used without their knowledge to buy goods or services with no intention to pay
  • - Subscription fraud by falsifying data like ID credentials to purchase handsets/devices without any intention to pay
  • - Account takeover using genuine customer details that are obtained illegally and used to order handsets/devices
  • - Payment fraud using stolen credit or debit cards or counterfeit cards to purchase handset/devices
  • - Credit muling or proxy fraud whereby a genuine customer colludes with the fraudster to get goods or services without any intention of paying
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