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Breakthrough Argyle Data Application Successfully Predicts Mobile Subscriber Creditworthiness

By   /  August 10, 2017  /  No Comments

by Angela Guess

According to a new press release, “AI/machine learning company Argyle Data™ has successfully concluded a series of trials with European and Latin American operators, using new algorithms and neural network architectures analyzing real carrier data to accurately predict subscribers’ intention and ability to pay monthly service bills. ‘Our solution is a quantum leap in subscriber validation. It not only identifies undesirable subscribers at the time of signing on for a mobile service, but also successfully predicts delinquencies in the following 60 – 90 days. This breakthrough is demonstrably more accurate than all other approaches and redefines the cost model for subscriber credit checking,’ said Vikash Varma, President and CEO at Argyle Data. Approximately 40% of all operator bad debts result from subscription fraud and default. Existing credit rating and analytics systems have not been able to identify high risk applicants from genuine subscribers. Even with limited data sets during very short time frames, Argyle Data achieved over 70% accuracy in identifying defaulters – an unprecedented result in reducing subscriber-related losses.”

The release goes on, “This solution is built upon Argyle Data’s existing machine learning (ML) applications that predict subscription fraud, stolen or false identities, SIM fraud, dealer scams, and handset theft schemes categorized as ‘Never Pay’. In this application, Argyle Data’s models are designed specifically to provide mobile carriers with early insight into future credit and payment issues by predicting the financial ability and future payment profile of subscribers. In the recent trials, the models were focused on predicting payment default sixty and ninety days out. ‘Creating the features requires a combination of mobile domain expertise and Data Scientist background to extract the relevant information from the data,’ said Padraig Stapleton, VP Engineering at Argyle Data. ‘This allows our models to predict the way that subscribers’ behavior will change and accurately identify those individuals with a high likelihood of payment issues in the future’.”

Read more at Marketwired.

Photo credit: Argyle Data

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