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Using Big Data Analytics in Fraud Detection

As digital transformation has continued to accelerate, so has the sheer number of fraudulent activities within the Telecommunications industry. With fraud becoming a real threat to the profitability of Telcos, many are turning to big data analytics to assist in combating fraud. In the blog below, we unpack how fraud impacts Telcos and uncover the importance of fraud detection and prevention in today’s digital landscape.

Fraud Detection

Understanding the Significance of Big Data Analytics in Fraud Detection and Prevention

When discussing fraud being a severe threat to Telcos, it’s important to showcase how this affects revenue. It is estimated that the global revenue loss has reached a staggering 2.2% or $40 billion due to fraud. For Telcos, this translates into a monumental loss in profitability and will ultimately affect business growth.

There are various types of fraud and threats which are recorded constantly within the Telco network. Four of the most popular types include:

  • Stolen Identities – Also called SIM Jacking, criminals take control of a person’s SMS and phone calls by switching a phone number to another they are in control of to gain access to all OTPs and SMS verifications to take over all customer accounts, including social media and banking.
  • Subscription Fraud – Occurs when fraudsters sign up for contracts using stolen IDs and stolen credit card numbers, or when subscriptions are obtained fraudulently on the network.
  • SIM Box Fraud – Also known as interconnect bypass fraud, takes advantage of a termination rate to make cheaper phone calls. These criminals use SIM cards from a local carrier and reroute international calls using a SIM box or GSM gateway, therefore making long-distance calls much cheaper for the callers. This type of fraud is estimated to cost telecom operators $2.7B in lost revenue per year.
  • Wangiri – Wangiri Telecommunications fraud involves striking curiosity in customers by calling them, letting the phone ring once, and hanging up. The customer will ring back, unwillingly calling an expensive premium number that the fraudsters control.
  • Access Point and Tarif Misuse – Involves the unauthorised usage of traffic through the various access points and tariffs envisioned for customer services which is generally zero-rated, such as banking channels.

With the threat of continuous fraudulent activities taking place within the network, Telcos need to ensure that they have the correct systems to manage and identify fraud to prevent these kinds of abuses. Big data analytics is key to this as these advanced analytics can detect unusual behaviour and anomalies within the network. This assists with minimising financial losses and preventing fraud. Let’s examine how big data analytics makes this possible.

Detecting Fraud through Data-Driven Insights

One of the significant benefits of utilising big data analytics is that you can access data-driven insights. These insights enable the identification of patterns and trends related to fraudulent activities. How does this work? The answer lies in the utilisation of network usage data.

Network usage data plays a critical role in revenue assurance and fraud detection. It provides essential insights into how consumers use the network, how it performs, and any problems or issues related to network usage.

Network usage data includes Call Detail Records (CDRs) and other event data related to billing usage and activities within the network. This data provides insight into how users utilise a phone system, the internet, video calls and SMS.  There are hundreds of fields that capture information related to the number that made the call, the number that received the call, the date and time of the call, and the duration, as well as usage and diagnostic information related to call quality, handset identification, the ID of the network equipment (cell tower) that facilitated the event, the reason for call terminations, and more.

This type of information and data is the key to fraud detection. When modelled and utilised correctly using advanced analytics, this data can be transformed into critical insights that will provide the Telco with information related to network usage, capacity, billing, and performance, thus making it easier to identify any problems, issues, or opportunities, especially where fraud detection is concerned.

From the above, it is clear that having the right kind of network data is essential to accessing data-driven insights. Now, let’s examine some advanced analytics techniques that are used to transform this data into insights to prevent and stop fraud.  

Prevention with Predictive Analytics

Predictive analytics plays a crucial role in fraud detection by helping Telcos identify and prevent fraudulent activities more effectively. Telcos can leverage predictive analytics techniques and tools to analyse large volumes of data and detect unusual patterns or anomalies that may indicate fraudulent behaviour.

The predictive analytics algorithms can analyse historical data to establish normal usage patterns for subscribers, including call volumes, data usage, locations, and more. Any deviation from these established patterns can then trigger alerts for further investigation. For example, if a subscriber suddenly starts making an unusually high number of international calls or there are significant data usage spikes, it could be a sign of fraudulent activity.

Predictive models can be deployed to monitor real-time network traffic and subscriber activity. This means that alerts can be generated immediately for investigation when suspicious activities are detected, such as multiple failed login attempts, calls from unusual locations, or sudden changes in usage behaviour.

Predictive analytics are also often used to analyse network traffic patterns to identify call anomalies. For example, when fraudsters use techniques like SIM box fraud, predictive analytics can be used to identify this activity by detecting irregularities and analysing call patterns and call termination points.

From the above, it is clear that predictive analytics provides insight into how fraudsters operate. By utilising these advanced analytics models, Telcos can learn from new data, improve their ability to detect fraud and prevent fraud from reoccurring in the future.

Fraud Detection

Near Real-time Monitoring and Anomaly Detection

We briefly mentioned how near real-time monitoring assists in anomaly and fraud detection within the realm of advanced analytics above, but let’s unpack this in more detail.

This type of monitoring ensures that data is continuously collected and analysed. This means that any suspicious activity can be detected immediately, allowing for a swift response and intervention.

Near real-time monitoring and advanced analytics also enable the analysis of user or transaction behaviour over time. This can include factors like the time of day, location, spending patterns, and more, so if there are any significant deviations from the usual behaviour, an alert will be triggered. The main benefit of this kind of monitoring is that it allows for almost immediate responses to suspicious activities. This might include flagging a transaction for manual review, blocking a potentially fraudulent account, or taking other appropriate actions to prevent further damage or financial losses.

On the other hand, anomaly detection starts by creating a baseline of normal behaviour. Advanced analytics algorithms analyse historical data to understand what is considered “normal” behaviour for different users, transactions, or systems. This forms the baseline, and any deviation from this baseline can be flagged as a potential anomaly and investigated.

When real-time monitoring and anomaly detection are combined, they can identify subtle and previously unknown patterns of fraudulent behaviour. In this way, near real-time monitoring and anomaly detection are powerful tools in advanced analytics for fraud detection. They provide the ability to detect known and unknown fraud patterns and respond swiftly to potential threats, making them essential components of modern fraud detection systems.

Conclusion

With fraud being such a prevalent threat to Telcos, it is vital to have an advanced analytics solution with big data, predictive analytics, near real-time monitoring and anomaly detection capabilities. These big data analytics tools make it easier for a Telco to identify, manage and prevent fraud within the network and, in doing so, reduce damages, decrease financial losses and avoid customer churn due to fraudulent activities. As the digital landscape continues to accelerate, grow and diversify, so will fraudulent activities. Telcos need to have innovative fraud detection software in place, such as Adapt IT Telecoms fraud detection solution powered by CDRlive technology. For more on this solution, visit https://telecoms.adaptit.tech/resources/analytics-white-paper/

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