Anomalies are a widespread problem across many businesses, and the telecommunications sector is no exception. Anomalies in telecommunications can be linked to system effectiveness, unauthorised access, or forgery, and therefore can present in a number of telecommunications procedures. In latest years, artificial intelligence (AI) has been become more to overcome these issues. Telecommunication invoices are among the most complicated invoices that may be created in any sector. With such a large quantity and diversity of goods and services available, mistakes are unavoidable. Products are made up of product specifications, and the massive amount of these features, as well as their numerous pairings, gives rise to such diversity (Tang et al., 2020). Goods and services – and, as a result, the invoicing process – are becoming even more difficult under 5G. Various corporate strategies, such as ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and large machine-type communication, are being addressed by service providers. Alongside 5G, the 3GPP proposed the idea of network slicing (NW slice) and the related service-level agreements (SLAs), adding still another layer to the invoicing procedure's complexities.

How do network operators discover invoice irregularities?

Invoice mistakes are a well-known truth in the telecom business since they contribute to invoicing conflicts and are one of the primary reasons for customer turnover. Invoice mistakes have a significant monetary and personal impact on service providers. To discover invoice abnormalities, most network operators use a combination of traditional and computerized techniques. The manual method is typically dependent on sampling procedures that are determined by company regulations, availability of materials, personal qualities, and knowledge. It's sluggish and doesn't cover all of the bills that have been created. These evaluations can now use regulation digitization to identify patterns and provide additional insight on massive data sets, thanks to the implementation of IT in business operations (Preuveneers et al., 2018). The constant character of the telecom business must also be considered, and keeping up would imply a slowdown in the introduction of new goods and services to the marketplace.

Management AI - Anomaly Detection And Machine Learning

How AI and Machine Learning can helps developers overcome the invoice anomaly detection?

An AI-based system may detect invoicing abnormalities more precisely and eliminate false-positive results. Non - compliance actions with concealed characteristics that are hard for humans to detect are also easier to detect using AI (Oprea and Bâra, 2021). Using the procedures below, an AI-system learns to recognise invoice anomalous behaviour from a collection of data:-Data from invoices is incorporated into an AI system.

  1. Data from invoices is incorporated into an AI system.
  2. Data points are used to create AI models.
  3. Every instance a data point detracts from the model, a possible invoicing anomaly is reported.
  4. The invoice anomaly is approved by a specific domain.
  5. The system takes what it has learned first from the activity and applies it to the data model to make future projections.
  6. Patterns keep collecting throughout the system.

Before delving into the details of AI, it's vital to set certain ground rules for what constitutes an anomaly. Anomalies are classified as follows:-

  • Point anomalies - A single incident of data is abnormal if it differs significantly from the others, such as an unusually low or very high invoice value.
  • Contextual anomalies - A data point that is ordinarily regular, but it becomes an anomaly when placed in a situation.
  • Collective anomalies - A group of connected data examples that are anomalous when viewed as a whole but not as particular values. When many point anomalies are connected together, they might create collective anomalies (Anton et al., 2018).

Benefits of anomaly detection

Implications of AI and Machine Learning in Anomaly Detection

All sectors have witnessed a significant focus on AI and Machine Learning technologies in recent years, and there's a reason why AI and Machine Learning rely on data-driven programming to unearth value hidden in data. AI and Machine Learning can now uncover formerly undiscovered information and is the key motivation for its use in invoice anomaly detection and a significant part of what makes it so compelling (Larriva-Novo et al., 2020). It can assist network operators in deciphering the unexplained causes of invoice irregularities. Furthermore, it can provide genuine analysis, increased precision, and a considerably broader range of surveillance.

Artificial intelligence (AI) presents a number of challenges.

The data that is given into an AI/ML algorithm is as strong as the algorithm itself. When implementing the invoice anomaly algorithm in operation, it will need to react to telecommunications data. Actual data may alter its features or suffer massive reforms, requiring the algorithm to adjust to these changes. This necessitates continual and rigorous monitoring of model monitoring and management. Throughout the field, there are also two typical challenges: a loss of confidence and data skew. Unawareness breeds a distrustful environment. Clarity and interpretability of predicted results might be beneficial, especially in the event of billing discrepancies (Imran, Jamil and Kim, 2021).


Telecom bills are among the most complicated payments created by any sector due to the complication of telecommunications agreements, goods, and billing procedures. As an outcome, billing inconsistencies and mistakes are widespread. The existing technique of manually verifying invoices or using dynamic regulations software to detect invoice anomalies has limits, such as a limited number of invoices covered or the inability to identify problems that have not been defined as rules. AI and Machine Learning can assist with this since they could not only encompass all invoice information but also discover different anomalies over time (Podgorelec, Turkanović and Karakatič, 2019). Besides invoice anomalies, a rising number of service providers are beginning to leverage AI and Machine Learning technology successfully and quickly for a diverse range of applications.


  • ‌Anton, S.D., Kanoor, S., Fraunholz, D. and Schotten, H.D. (2018). Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set. Proceedings of the 13th International Conference on Availability, Reliability and Security.
  • Imran, Jamil, F. and Kim, D. (2021). An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability, 13(18), p.10057.
  • Larriva-Novo, X., Vega-Barbas, M., Villagrá, V.A., Rivera, D., Álvarez-Campana, M. and Berrocal, J. (2020). Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets. Applied Sciences, 10(10), p.3430.
  • Oprea, S.-V. and Bâra, A. (2021). Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Computers & Electrical Engineering, 94, p.107329.
  • Podgorelec, B., Turkanović, M. and Karakatič, S. (2019). A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection. Sensors, 20(1), p.147.
  • Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W. and Ilie-Zudor, E. (2018). Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study. Applied Sciences, 8(12), p.2663.
  • Tang, P., Qiu, W., Huang, Z., Chen, S., Yan, M., Lian, H. and Li, Z. (2020). Anomaly detection in electronic invoice systems based on machine learning. Information Sciences, 535, pp.172–186.

Learn more about Nife to be informed about Edge Computing and its usage in different fields