Rely on our updated adaptive fraud detection system.
Sophisticated algorithms and AIs based on machine learning can analyse and compare enormous amounts of information within seconds, to evaluate potential fraudulent patterns and anomalies, to which they alert you immediately. Protect and stabilise your profits with us.
Machine Learning Fraud Detection System (ML FDS) by Adastra
Our adaptable, machine-updated fraud detection model provides you with:
A modern system where there is no need to manually manage rules and updates.
Reduced number of false positives, increasing the efficiency of suspicious event verification.
Detection of risk events both inside and outside the organisation.
Detection of anomalies and fraudulent behaviour - can detect a stolen phone and block calls from it.
The system, which is based on neural networks, works with both the operator's operational data and external sources. It facilitates the analysis of different sources of information (network data, voice biometric data when contacting a customer line, or data from social networks) and correlates data. It can work independently, in parallel with an existing rule system as an additional indicator, or as another variable to calculate an existing score.
Identify fraudulent behaviour immediately
Work with all available data. We process two basic types of data to allow you to evaluate fraudulent behaviour:
customer information, such as the average amount in call charges per financial year, payment behaviour, and business size,
network event data from which, for example, time, location, length and phone call participants are stored.
With the development of OTT services in telecommunications, especially mobile payments, the value of potential risk is growing significantly. For example, abuse of mobile payments can cause much greater damage than the abuse of a stolen phone by calling premium lines.
Adastra detects suspicious behaviour using sophisticated algorithms capable of handling large amounts of unstructured data from different sources in real time. Prevent fraudulent behaviour with us by detecting it early and by constantly debugging algorithms to detect it.
Absolute indicators - these are groups of limits and rules applied to certain types of services. In practice, for example, rules are applied to colour lines (green with the 800 prefix) or premium lines starting with 90x, calls to exotic destinations, or excessive calls from a newly established telephone line for a customer without any history.
Relative indicators - these try to reflect changes in the behaviour of the customer. Each user or group of users of a telephone network has a profile that is constantly developed and compared with its current behaviour in a geographical context.
35-40 billion dollars reach annual losses worldwide due to fraudulent behaviour. These losses grow at a rate of 11-25 % per year. By comparison, total revenue growth in telecommunications is between 3-8 % per year.
Don't take any risks with your customers' trust
And minimise the number of "false alarms". Our systems analyse suspicious behaviour patterns and generate alerts. They also use learning algorithms based on rule and data mining principles which automatically change the set parameters, based on continuous evaluation of the success of their own predictions (by comparing prediction and reality). In this way, we continually increase our success rate in detecting suspicious cases and constantly improve the accuracy of alerts. At the same time, we also minimise possible false suspicions (cases where correct behaviour is labelled as fraudulent).
Would you like to get a solution customized to the needs of your company? Contact us today.
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