Fraud Detection and Prevention: How to Adapt to the Dynamic Threat

The telecom world is changing, all while enabling the digitization of services across different sectors; these changes, however, are increasing the fraud risks and threats within the telecom world itself. Fraudsters have long developed methods and practices to exploit the telecom network. Losses from telecom fraud have a direct impact on a carrier’s margin. It also impacts the brand image and their customer’s experience. A holistic fraud management system help safeguard operators’ revenues and continue to provide superior quality of service to their customers. This allows the carrier to compete more effectively and profitably.

Rule-Based Systems Aren’t Enough

A traditional approach to fraud detection has been through Rule Engines, which could be:

• If-Else Conditions
• Thresholds
• Expressions
• Evaluating Data Patterns

These are widely known as deterministic solutions where an event triggers an action. The biggest pros and cons of this approach is that human intervention is needed to feed the logic.

Key Attack Vectors

The world of telecommunication fraud is constantly changing. There has been an increase in critical attack vectors, such as:

• Fake and synthetic IDs: Fraudsters now have a wider choice of alternatives for obtaining ID documents, whether through phishing or a Rent-an-ID business. Since parts of the IDs are valid; this makes fraud detection much more difficult.
• eSIMs: While eSIMs, or virtual SIM cards, are more secure since they are more difficult to clone or steal, they are still vulnerable to malware and social engineering assaults.
• Social engineering: The pandemic has dramatically increased the social engineering attacks. Social engineering fraudsters use a variety of means to carry out their attacks, some of which are: Phishing, Spear Phishing, Caller ID Spoofing, etc.

AI as a Fraud Detector

Artificial Intelligence (AI) is not new, and it has been around for decades. However, with the advent of big data and distributed computing that is available today, an effective Fraud Management (FM) strategy includes 3 important pillars: Detect, Investigate & Protect. We believe AI can positively influence all the 3 pillars of fraud management, from reducing false positives to helping in mining root cause analysis to creating enhanced customer experience in protection.

AI/ML brings in the below benefits:

• Higher Accuracy – Because AI can learn and adapt to Business scenarios faster, AI can significantly increase True Positive ratio.
• Reduced time-to-detect – How fast a telecom fraud event can be detected.
• Self-Learning – How over a period changing business scenarios and seasonality in data can be adopted to fraud detection and prevention.
• Fraud Intelligence– How customer or any other entity behaviors can be learned and categorized for better fraud detection and prevention.
• Proactiveness – Ability to mine for unknown patterns not seen in the data earlier, thereby enabling proactive mitigation of telecom fraud.

Fraud Detection in the Modern World

CSPs need AI with strong machine learning skills to handle extraordinarily massive volumes of data while also coping with the high levels of agility and complexity demonstrated by fraudsters from all over the world to effectively manage and fight telecommunication fraud in the modern landscape.

CSPs indeed need a robust fraud management system that incorporates advanced technologies such as AI/ML to combat telecom fraud and stay ahead of fraudsters.

With ML Algorithms being a part of the fraud management system, it will be able to mine data from historic fraudulent behaviors and create models. These models are then used to evaluate real production datasets to score whether a certain activity is fraud or not. An advantage is that these models are very good at looking at the datasets from multiple dimensions and measures at the same time and concluding whether the event is fraud or not.

The application of AI has its own significant challenges and requires a new frame of thought, however looking at the Data Tsunami that has hit the fraud management teams, it looks an AI pro approach would only help Fraud Management teams to scale further.

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