How AI can help in detecting, predicting & preventing telecom fraud
Telecommunications companies have typically faced many challenges arising from a variety of issues, including network operation and infrastructure concerns, complicated networking systems, unsuitable resource utilization, customer support problems, network losses, and ever-increasing bandwidth necessities.
In such a scenario, AI/ML has been touted as a must-have capability that any fraud team has to have in order to safeguard their business and customers from any form of risks. In fact, in a recent report by Gartner, it was estimated that by 2022, new implementations of ML within CSP fraud management would help reduce fraud losses by 10%.
As AI solutions continue to become the norm across a variety of issues, it’s helpful to consider just how valuable it can be for communication service providers to support Telecom Fraud Prevention.
It’s not strictly a case of if it should be used, or when it can be used – instead, it’s a case of who can use AI; what they can use it for; how they can use it, and why they would.
Considering the way, it is designed, managed and implemented, AI-based Fraud Management can be greatly advantageous and might help you take long strides forward toward successful Telecom Fraud Management.
Automation and artificial intelligence in telecom can help decrease fraud and increase the possibility of generating revenue, strengthen customer relationships, and improve network capabilities.
Conventional telecom security systems simply identify commonly occurring problems but fail in detecting or forecasting potential future threats.
Telecom fraud could appear in many forms, including subscription, voicemail fraud, identity theft, international revenue-sharing fraud, and voice phishing calls. Telecommunications data include highly susceptible information, such as source number, destination number, call duration, call type, location, area, and account billing data. With AI-based Machine learning techniques and advanced analytics, fraud management teams can:
- Track fraudsters faster and eliminate revenue loss caused by arbitrage
- Enable KPI tracking and anomaly detection with minimal analyst involvement
- Differentiate fraudsters from genuine customers by identifying important features to reduce false positives and enhance customer experience
Artificial Intelligence in telecom has made it much easier to set up tools that can detect and respond to fraudulent activities on the network with network optimization AI. Additionally, this network optimization AI largely reduces response time, allowing telecom businesses to eliminate the threat before they can exploit internal information systems. This is a prominent example of how predictive prevention using AI can keep both companies and customers safe from fraud.
In a nutshell, AI can learn to tell fraudulent operations from legitimate ones by using big data better and faster than humans ever will be able to.
AI fraud detection tools are way more effective than humans. They can process a sea of information faster than a team of the best analysts ever could. What’s more, ML algorithms can detect patterns that might appear unrelated or go unnoticed by a human. By exploring and studying tons of cases of fraudulent behavior, ML algorithms determine the stealthiest fraudulent patterns and remember them forever.
As online fraud becomes ubiquitous, they also evolve. Both fraudsters and businesses utilize advanced tools and compete to get a step ahead. In such a critical environment, companies need to examine way more information than they are usually equipped to handle to fight fraud. Even if a business hires a team of top data scientists, they cannot track fraudulent attempts as fast as they occur. AI Fraud detection models come to the rescue, being able to operate 24/7 and interpret enormous amounts of data quickly.
With AI tools integrated into the system, new metrics come into play. Understanding these metrics will help assess how AI can help increase the efficiency of fraud management.
- Higher Accuracy – Because AI can learn and adapt to business scenarios faster, AI can significantly increase the True Positive ratio
- Reduced time to detect – How fast a fraud occurrence can be detected
- Self-Learning – How over a period changing business scenarios and seasonality in data can be adopted for Fraud detection
- Fraud Intelligence– How customer or any other entity behaviors can be learnt and categorized for better fraud detection
- Proactiveness – Ability to mine for unknown patterns not seen in the data earlier
The conventional fraud detection model is established on a static rules-based system, these systems have been effective for a long time, but they are unsuitable for modern digital environments due to their dependence on human labor. But naturally, top data scientists are expensive, and their work takes more time. What’s more, even leading experts create rules based on their limited knowledge and experience. Such rules can grow to massive proportions and get very complex that it’s rarely understood by an outsider when needed. Fraud detection using AI can solve all these issues. It can outperform traditional fraud detection systems in terms of speed, quality, and cost-effectiveness. An unsupervised AI system can process data autonomously and constantly to update its models and patterns in real-time. As the data assets of the telecom business become overwhelming, higher workloads help AI models learn better and more precise fraud detection algorithms.
Machine Learning and AI are used in behavioral analytics to analyze and predict behavior at a basic level across all aspects of a transaction. Profiles that observe attributes of each user, merchant, account, and device are kept track of the data. These profiles are revised in real-time with each transaction, allowing analytic features to make forecasts of future behavior. Profiles are very useful since they provide current data of activity, which can help prevent transaction defection due to annoying false positives.
Telecom is one of the most vulnerable industries when it comes to fraud and undergo the highest financial losses due to said risks. Yet, despite AI’s potential to fight fraud, it’s not straightforward for organizations to move to AI. Once the shift has occurred though, we can look forward to AI improvements that will make fraud-fighting more effective.
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