‘Never Pay’ Prevention

Argyle Data provides real-time predictive analytics to detect and prevent subscription fraud.

Subscription Fraud Platform
PREDICTIVE ANALYTICS FOR SUBSCRIBER VALIDATION

Argyle Data’s predictive analytics solutions use supervised and unsupervised machine learning and neural network architectures to provide operators with unprecedented capabilities to detect and prevent high-value subscription fraud scams, crack down on dealer/SIM fraud, and accurately predict the creditworthiness of new subscribers.

Benefits of Argyle Data over traditional systems:
Existing customer qualification solutions allow too many bad risk subscribers to slip through the net. Credit checking services have limited insights into customer credibility, and most operator solutions are neither systematic nor automatic. Argyle Data’s application works in real time to enrich the credit rating process, identify fraud and provide an accurate, instant platform for distinguishing high-risk applicants from genuine customers.

  • Supervised + unsupervised machine learning delivers previously unachievable levels of detection, prevention, and prediction
  • New visibility into evolving fraud methods
  • Exponential reduction in bad debt, subscriber fraud, and ‘Never Pay’

Machine learning algorithms and neural network architectures provide accurate, predictive insights on each subscriber.

Subscription Fraud Platform:
Detect and Prevent High-Value Scams

Approximately 40% of all carrier bad debts are related to subscription fraud. In high-ARPU markets, the biggest losses are from smartphone theft or ‘never pay’. In less wealthy markets, SIM fraud is a more pressing issue. Argyle Data’s application works in real time to weed out fraud types, enrich the credit rating process and identify high risk applicants, and improve the sign-up rates of valid subscribers.

Why use Argyle Data?

Argyle Data’s machine learning approaches and domain expertise round out credit rating scores with 360° demographic and social information that provides a holistic, detailed, and accurate picture of each aspiring mobile subscriber in real time.

Benefits of our machine learning approach:

Our machine learning algorithms capture geo-spatial features from the carrier’s billing database and social network analysis from CDRs. These are integrated with additional data sources, accurately predicting fraud cases through our state-of-the-art ensemble learner:

  • Proprietary augmented features to distinguish signal from noise
  • Machine learning handles extreme class-imbalance
  • Algorithms can be tuned to track the business objectives/risk appetite of each operator
  • Operators can also tune the algorithms to assign monetary risk values to subscribers

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Solution Brief: Subscription Fraud Platform
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Approximately 40% of all carrier bad debts are related to subscription fraud or subscriber default.

Argyle Data Predictive Analytics for Mobile Subscriber Validation

Subscription fraud and subscriber default are a huge problem for mobile carriers. Credit checking services have limited insights into customer credibility, and most operator solutions are neither systematic nor automatic. Argyle Data’s machine learning/neural network application allows operators to accurately pinpoint defaulters before issues arise.

Why use Argyle Data?

This solution builds upon the success of Argyle Data’s existing machine learning applications that leverage our technical and domain expertise to predict subscription fraud of all types. With this model, Argyle Data uses new machine learning algorithms and neural network architectures that analyze mobile carrier data to accurately predict subscribers’ intention and ability to pay monthly service bills. Even using limited data sets during very short time frames, we achieve over 70% accuracy in identifying defaulters – an unprecedented result in reducing subscriber-related losses.

Benefits of predictive analytics:

To build its predictive analytics model, Argyle Data has added a layer of processing to its existing subscriber fraud solutions using a neural network architecture:

  • Algorithms are enriched using multiple machine learning techniques on mobile carrier data sets
  • Different machine learning approaches are applied to complement each algorithm’s strengths and weaknesses
  • Operators achieve exponentially better results and more transparency on the outcome
  • Future subscriber payment issues are predicted with a high degree of accuracy as far ahead as two to three months before default

Want to Learn More?

Solution Brief: Subscriber Validation

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