What are the benefits of using a telecom fraud management system?
Telecom fraud has been significantly on a rise over the years. This is evident from the recent CFCA Survey 2021 which states the fraud loss is estimated to be $39.89 bn. In the past, organizations had to take a basic approach to fraud prevention, using sedentary business rules and rudimentary analytics to find anomalies to create alerts.
Data could not be checked through automation, and neither could investigators monitor transactions and crimes in real-time; they could only do so after the attack. Usually, fraud detection was also more like “pay and chase”, because the criminal was away by the time fraud was noticed.
To combat fraud, newer technologies have been developed to predict traditional tactics, discover new schemes and interpret increasingly refined organized fraud. This implicates better strategies than just standard analytics; it applies predictive and adaptive techniques – including a form of AI known as machine learning. By integrating all data sources with real-time monitoring and risk profile analysis to achieve precise fraud risk factors, fraud prevention has evolved to turn the tides of losses.
With the growing sophistication of state-sponsored terrorism, skilled criminals and cellar crooks are becoming harder to apprehend, follow, reveal and control. Fraud detection in today’s world involves an exhaustive approach to match data parameters with activities to find what is anomalous. Fraudsters have conceived sophisticated tactics, so it is crucial to stay on top of these changing approaches of toying with the system.
Many times, cybersecurity breaches stimulate fraudulent actions. Take, for example, retail or financial services: Once a mainstream, real-time transaction monitoring is now a baseline necessity, not only for financial transactions but for digital activity data including authentication, session, location and device.
The fraud detection system that you choose should be capable of learning from complex data patterns. It should use sophisticated judgment standards to better manage false positives and analyse network relationships to see a holistic view of the activity of fraudsters and criminals. Combining machine learning strategies like deep learning neural networks, extreme gradient promoting and vector machines – as well as proven methods such as logistic reversion, self-organizing maps, random forests and getups – has proven to be very effective than approaches based on just rules.
What is a Fraud Management System?
A fraud Management system should be able to automatically watch transactions and events in real-time to detect and prevent fraudulent activities occurring in-house, online or in-store. It should stop unscripted access to sensitive company and customer data. The suspicious transaction or event is flagged and can be manually inspected through a management dashboard.
Fraud Management system prevents illicit activities related to payments, purchases, and chargebacks. It is employed to secure web, mobile and app-based financial transactions. It helps contain digital payment fraud, and account appropriation by validating identities sometimes using two-step authentications, and by identifying malicious logins, and bot activity.
A fraud Management system is also used to prevent insurance fraud and money laundering. It will help guarantee compliance with security and data privacy regulations.
It can scan internal and external data, employees, customers, transactions, events, and databases. Detection and deterrence are based upon rules or predictive models.
A fraud management system should increasingly use AI for Predictive Analytics which identifies imitation patterns. Ai is also employed for Customer Analytics which detects deviations from a customer’s usual behaviour on a site and Social Media Analytics which gathers information from social media and blogs. These patterns are processed to help identify fraudulent behaviour. In the future behavioural biometric capabilities will be readily integrated into fraud detection software. This includes analyzing a user’s mouse gestures, typing speed and routine to detect deviations from the norm as an indicator of potentially fraudulent activity. Real-time biometric detection will prevent identity theft and account takeovers.
AI-driven fraud prevention can emulate an experienced fraud analyst, allowing fraud prevention teams to focus on other key business strategies. This progressive solution stays ahead of arising fraud trends while empowering businesses to control outcomes, lower manual review rates, reduce chargebacks, and increase order acceptance rates. With this enhanced flexibility, businesses can determine and adjust their risk tolerance. Businesses also attain the data they require to understand buyer statistics and patterns. These insights yield business intelligence that can advise future strategies.
Telecom Fraud Management
Telecom revenue leakage and fraud are rapidly advancing as one of the most dangerous waters to linger in among telcos. This has led to huge revenue losses for operators across the globe, with the emergence of OTT data services, cut-throat competition for customer retention, and a steep drop in voice and SMS, revenue has harshly impacted the bottom line of telcos worldwide. In addition, smart innovations such as IoT and 5G in the telecom sector have driven rapid uptake among customers, however, the threats and vulnerabilities among linked applications, devices, payment gateways, and industrial integrations directly affect revenue and market share for telecom companies. With the deployment of Fraud Management System, environments will be able to keep up, and holistically safeguard businesses
Distinct advantages of deploying AI machine learning into telecom fraud management are:
- Improved automation that directs to rarer manual reviews
- The faster rate in making a risk assessment by identifying patterns in data
- Increased accuracy to categorize good orders versus fraudulent orders
- Enables data to be better classified and trends to be identified quickly
- Efficient utilization of resources is achieved as models are constantly updated with new data and feature sets.
A fraud management system can also evaluate, continuously monitor user behaviour and estimate risk factors to identify potentially fraudulent purchases, transactions, or access.
Benefits of Fraud Management system :
1. Proactively monitor for potentially fraudulent or high-risk events
Real-time fraud management of internal and external data, employees, customers, transactions, events, and databases. Real-time transaction screening and review. Pattern recognition / Anomaly detection is also boosted with AI integration, it detects emerging fraud to uncover anomalies quickly and more accurately. The extraction, automation and processing of the events are done with little or no human involvement. FMS can interface with different types of data sources to assure visibility and usage on a broad range of services.
2. Compute transactional risk factors to determine the legitimacy
Custom fraud parameters are established with organization-specific parameters and verges to trigger fraud prevention actions. Investigations into fraudulent detections to cross-reference with the usual user behaviour. Security management through the dashboard to manage role-based user access to transactional data to prevent tampering or hacking. It increases sales and revenue by helping businesses accept more promising orders.
3. Detect illegitimate transactional behaviours online
A powerful dashboard provides the capability to review transactions, identify patterns, provide insights, and make informed decisions on any reports. It controls business effects through customizable, goal-based risk thresholds.
4. Provide alerts and analysis tools for administrators
Internal fraud monitoring, where the software can review the internal actions to determine the need for investigation. It reduces fraud operations costs by lowering chargeback and manual inspection rates.
Dashboard alerts and the process of fraud being detected within the FMS is a key tool for a Fraud Manager. This tool allows the Fraud Analysts to view critical performance indicators, analyze whether fraud detection targets are being exercised, and review the performance metrics to ensure cases are being managed and resolutions are conducted properly.
5. Ensure compliance with data privacy and security regulations
Preemptive check for Data security breaches and privacy regulations compliance nuisances. These compliance tools ensure all fraud incidents are identified, recorded and tracked in a location. This information can be used to list out or block fraud syndicates and fraudsters, where links are recognised between new and old cases.
Additionally, it provides storage for all the information related to a case to be stored at a later stage. This also enables tracking the performance of the Fraud Analyst cases for further optimization of processes that are being followed.
6. Increased coverage to prevent fraud
A lot of the new services that are being rolled out by the telcos do not necessarily follow the transaction record principle, services such as IPTV, IoT and those that are being rolled out on 5G, involve a plethora of technologies and multiple third-party players. While each of these services may be inherently secure, they typically tend to have vulnerabilities at the seams or integration points. The new types of fraud attacks on these services exploit such vulnerabilities. By monitoring the signalling layer, fraud teams essentially create a safety net that can monitor across traditional offerings, new services, and anything in the future, thus greatly expanding the fraud team’s coverage from voice, messaging, and data.
7. Preventing Complex Fraud
As fraudsters evolve their attacks and tools, it becomes the prerogative of the fraud detection team to stay abreast and quickly evolve their detection and mitigation techniques; this is becoming extremely difficult using the traditional data sets that an FMS uses. The first principle methodology of reaching into the most basic of network traffic, namely signalling, gives the team the ability to build complex detection methodologies to identify and mitigate complex fraud.
Organizations that leverage machine learning is empowering their decision-makers with the ability to access data, understand its meaning and make informed decisions to stop fraud before it impacts the business’ bottom line and the overall brand. Risk and fraud administration managers face real-time obstacles when implementing a fraud strategy with real-life resources like cost, performance cycle, data permissions, detecting fair vs fraudulent orders, customization, and scalability with a business, etc.
Although we can tackle fraudsters through these ways, due to fast-moving telecoms systems and the need to launch complex products quickly to attract market share and preserve a competitive advantage, leading to weaker technical systems and higher risks that fraudsters utilize to keep the fraudulent business activities running. But fraud management systems equip a company to stay ahead of criminals regardless of the tools and tactics they use to commit fraud.
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