Improving invoice anomaly detection with AI and machine learning – Ericsson

Anomalies are a common issue in many industries and the telecom industry has its fair share of them. Telecom anomalies could be related to network performance, security breaches or fraud and can occur anywhere in multitude of Telecom processes. In recent times, AI is being increasingly used to solve these problems.

Telecom invoices represent one of the most complex types of invoices generated in any industry. With the huge number and variety of possible products and services errors are inevitable. Products are built up of product characteristics, and its the sheer number of these characteristics and the various combinations of them which leads to such variety.

To add to this is the billing process complexity, which presents a variety of challenges. A periodic bill is a regular billing process, but all other bills or bill requests results in a deviation from the standard process and may result in anomalies. Convergent billing also means its possible to have a single bill for multi-play contract(s) with cross-promotions and volume discounts, which can make billing a challenging task. Moreover, many organizations have a setup where different departments have different invoice and payment responsibilities which also complicates the billing process. Have a headache yet? Me too.

As you can see, the usage to bill or invoice journey is a multi-step process and is filled with pitfalls. And here comes even more trouble.

With 5G, products and services and subsequently the billing process become even more complicated. Service providers are gearing up to address varied enterprise models, such as ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), or massive machinetype communication (mMTC). The rollout of 5G also heralds a revolution of IoT devices.

3GPP with 5G has introduced the concept of network slicing (NW slice) and the associated service-level agreements (SLAs) another dimension that will add to the complexity of the billing process.

Its a known fact in Telecom industry that billing errors leads to billing disputes and is one of the leading causes of customer churn.

Fixing billing errors has a big cost and time impact on service provider financials. Most service providers have a mix of manual and/or automated processes to detect invoice anomalies. The manual process usually relies on sampling techniques based on organizational policies, resource availability, individual skills, and experience. Its slow and lacks coverage across the entire set of generated invoices. With the introduction of IT in business processes, such audits can leverage rule-based automation to find patterns and give insights on larger data sets; however, this also has the challenge of rules being nothing but encoded experience, which may result in high numbers of false positive alerts and the incorrect flagging of legitimate behaviors as suspicious. Rule identification is done by a domain expert. The dynamic nature of the telecom industry also has to be taken into account and keeping pace would mean slowing down the launches of new products and services in the market. As a result, I think its fair to say that traditional approaches are ineffective and inefficient.

An AI-based solution can more accurately identify invoice anomalies and reduce false positives. AI is also able to more easily identify noncompliant behaviors with hidden patterns that are difficult for humans to identify. An AI agent learns to identify invoice anomaly behavior from a supplied set of data using the following steps:

Before going deep into the aspects of AI, it is important to establish some boundaries on thedefinition of an anomaly. Anomalies can be broadly categorized as:

A single instance of data is anomalous if it is too far off from the rest, for instance, an irregular low or high invoice amount.

A data point otherwise normal, however when applied a context becomes an anomaly. For instance, an invoice having usage charges for a period when a user was in an inactive status.

A collection of related data instances that are anomalous with respect to the entire dataset but not individual values. For instance, a set of invoices having missing usage data or higher than usual charges for voice calls for a day or a period. A set of point anomalies could become collective ones if we join multiple point anomalies together.

Identifying the category of anomalies helps in the identification of suitable artificial intelligence or machine learning approaches. Machine learning has four common classes of applications: classification, predicting value using a regression model, clustering or anomaly detection, and dimensionality reduction or discovering structure. While the first two are supervised learning, the latter two belong to unsupervised learning. In machine learning, there are two types of categories based on the number of variables used to predict: univariate (one variable) or multivariate (where an outlier is a combined unusual score on at least two variables). Most of the analyses that we end up doing are multivariate due to the complexity of the billing process. The image below gives us a deeper look into machine learnings approach to anomaly detection.

During the past few years, all industries have seen a strong focus on AI/ML technologies and theres certainly a reason why AI/ML leverage data-driven programming and unearth value latent in data. Previously unknown insights can now be discovered using AI/ML which is the main driver behind using AI/ML in invoice anomaly detection and a big part of what makes it so appealing. It can help service providers understand the unknown reasons behind invoice anomalies. Moreover, it can offer real-time analysis, greater accuracy and much wider coverage.

The other benefit of AI/ML is the ability to learn from its own predictions, which are fed backinto the system as reward or penalty. This helps to not only learn the patterns of today but alsonew patterns which may arise in the future.

An AI/ML model is as only as good as the data thats fed into it. It means that the invoice anomaly model needs to adapt to telecom data when deploying the model in production. The models drift needs to be continuously monitored for effective prediction owing to the dynamic nature of real-world data. Real-world data may change its characteristics or undergo structural changes; thus, the model needs to continue to align with such changes in data. This means model life cycle management must be continuously ongoing and closely monitored.

A lack of trust and data bias are also two common challenges in this field. Its important that an organizations policies are designed to avoid data bias as much as possible. Lack of awareness seeds lack of trust. Transparency and explainability of model predictions can help; especially in cases of invoice anomalies (where it is important to explain the reason for an invoice being anomalous).

Yes, I believe it is. Ensuring that the billing process is fool proof and that the generated invoices are correct is a big task particularly in the telecoms industry. The current process of sampling invoices for manual verification or static rules-based software for invoice anomaly detection has limitations either in the coverage of the number of invoices or in not being able to discover errors which have not been configured as rules.

AI/ML can help here, as it can not only provide full coverage to all invoice data but can alsolearn new anomalies over a period. Ericssons Billing product has been in the process ofincorporating AI/ML technology to discover invoice anomalies and other appropriate usecases. Beyond just invoice anomalies, we are seeing a trend where a growing number ofservice providers have started to effectively and efficiently use AI/ML technology for varioususe cases.

Read the full eBrief on AI and invoice anomaly detection:

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Improving invoice anomaly detection with AI and machine learning - Ericsson