There was a time when the telecom operators depended on CDR analysis in order to identify any frauds. We are now witnessing advanced fraud management systems in place that can identify different fraud patterns based on improved self-learning technologies. In this blog we will discuss the AI/ML technologies that can be used to identify frauds.

Key AI/ML Techniques to Predict Telecom Frauds
Data Modeling

Supervised Model - Widely used form of machine learning for all types, based on transactions accurately marked. In this model, the transaction data is properly identified and marked as fraudulent or non-fraudulent transaction. This model is based on historical data set that understands the behavioral pattern of the data likely to be fraud or clean.

Unsupervised Models - Identifying the anomalous behavior where there is a lack of supervised model data or access to only a small set of supervised model data. In these cases, a form of self-learning must be carried out to understand the unorganized transaction data. Unsupervised models are designed to learn outliers that represent previously unnoticed usages of fraud. AI-based practices identify behavior anomalies by detecting transactions that do not adapt. For accuracy, these differences are calculated at the distinct level.

Real-time Behavioral Profiling - Machine learning that understands behaviors of Subscribers, Carrier, Trunk, IP, Called and Calling Numbers, Mobile Money Events, Dealers, Devices, and so on.

Supervised Machine Learning - Built and tested with large sets of non-fraudulent and fraudulent transactions.

Unsupervised Machine Learning - Self-learning AI that constantly learns and senses outliner events and behaviors.

Adaptive Analytics - Constantly updates the machine learning models based on feedback from fraudulent event analysis.