Communication Service Providers (CSPs) lose around $38 Billion to fraud every year. Check out this webinar to learn more about the Cloudera - Argyle Data real-time fraud analytics platform and how Telcos can utilize Apache Hadoop to drive down fraud.
Telco Fraud is a $38 Billion industry today – huge market and it is a global problem – It is not a third world problem
http://www.nytimes.com/2014/10/20/technology/dial-and-redial-phone-hackers-stealing-billions-.html?_r=0
Hackers had broken into the phone network of the company, Foreman Seeley Fountain Architecture, and routed $166,000 worth of calls from the firm to premium-rate telephone numbers in Gambia, Somalia and the Maldives. It would have taken 34 years for the firm to run up those charges legitimately, according to a complaint it filed with the Federal Communications Commission.
The firm, in Norcross, Ga., was the victim of an age-old fraud that has found new life now that most corporate phone lines run over the Internet.
http://www.ibtimes.com/inside-story-how-pakistan-took-down-fbis-most-wanted-cybercriminal-1860808
http://www.premiumrateinternational.com/countries.php
IRSF- International revenue share fraud (IRSF) - involves artificially inflating traffic to a foreign number. IRSF, is usually combined with premium rate service fraud. ISRF is driven by both connection charges and by the number of minutes consumed. Certain countries have particularly high interconnection fees and are a focus for IRSF. These fees are highest in the Caribbean (including Cuba) and many small countries in the Pacific.
Two individuals were arrested recently for gaining access to business telephone systems and using the systems to place international telephone calls to premium rate numbers. This cost the victims more than US$50 million.
Every year, the telco industry loses $38 Billion to fraud and Roaming Fraud takes the biggest hit: about $6 billion globally and almost $2 billion in North America and Western Europe.
Criminals are adopting newer techniques to perpetrate fraud quickly and more efficiently than ever before. A cyberfraud gang can set up, go to work and disappear in 24 hours or less, often before an operator even knows the fraud or arbitrage attack is happening. Modern, sophisticated attacks are mutating and continually evolving.
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Roaming Fraud: When roaming, the call data is collected by the visiting network, which can be a nearby operator just as easily as it could be one half way across the world. The call detail record (CDR) for these roaming charges doesn’t arrive to the home network until days sometimes weeks later, leaving a large window of opportunity for fraudulent attacks.
Domestic revenue share fraud pertains to the abuse of carrier interconnect agreements and is very similar to international revenue share fraud and premium rate service fraud. In all three scenarios, there is an artificial inflation of traffic to a premium rate phone number. The scheme is fairly simple: A fraudster gets hold of a premium rate service number – a phone number where a portion of the charges goes to the operator and not only the phone carrier like with regular phone numbers – and inflates the traffic to the service to generate more revenue.
Roaming fraud is the responsibility of the home network of the subscriber, providing that the visiting network meets its Service Level Agreement (SLA) commitments (typically 4 hours). This time window is critical, because:
If the SLA is met the financial responsibility is in the hands of the home network.
If the SLA is not met the financial responsibility is in the hands of the visiting network.
Many carriers are trying to defend themselves with legacy approaches and technologies that don’t work anymore.
Legacy systems rely on:
• Batch processing – Typically discovers threats 24 – 48 hours after it has occurred – discovers threats too late- Goal for operators should be to reduce the window from 24 hours to discovering fraud within seconds or minutes…
• They are Rules based - and discover only old known, patterns of fraud – Cannot detect net new attacks or they overload the analyst with false positives – So the real attack may be 1000 on the list of alerts
• Silos of data that sits across multiple systems – Legacy systems are able to look at only a fraction of data and can never really give you a 360 degree view of fraud.
In terms of data we are talking about an Average of 35 – 60 Billion CDRs a day & 1.5 Trillion events/ day for a Tier 1 Mobile organization
So how do you look for fraud signals in this mountain of data and bring out meaningful threat analytics? This is really where a Hadoop & Machine learning comes into picture.
Using Hadoop you can utilize all of your data and you look for fraud signals in more data, more often and in real-time
This is where Hadoop & machine Learning really excels in– You Need a Big Data platform to look at this scale of data in real-time and drive Machine learning & real-time analytics on this data.
Existing systems simply don’t work anymore. They either:
• Fail - Don’t discover new threats or “zero-day” fraud attacks
• Overwhelm – Bombard users with false positives
• Operate in Batch – Discovering fraud threats too late
Scale of Data in Telcos - Average of 35 Billion CDRs a day – for a NA Service Provider 1.5 Trillion events/ day –
This is really where a Big Data Platform or an Enterprise Data Hub comes into picture
You can discover Fraud that you didn't’t know existed earlier
You can also discover net new attacks
At this point I want to open up to a polling question, to try and understand from you – where you are with respect to the IoT journey within your organization.
We’ll give it about 20sec so that everyone can answer – I already see a lot of responses coming in
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Very consistent with what we see in the market
In line with what we hear from our customers as well.
This is where Hadoop & machine Learning really excels in– You Need a Big Data platform to look at this scale of data in real-time and drive Machine learning & real-time analytics on this data.
Cloudera has teamed up with Argyle Data to deliver a next generation, native-Hadoop, real-time, fraud analytics platform that is tailored for today’s Communication Service Providers. This enables is CSPs to take advantage of all the data they have at their disposal to easily and effectively identify fraud and revenue threats in real-time.
Real-Time Data Ingest: we can look at data from a multitude of sources including - mobile call packets, fixed call packets, VoIP and Data packets (including TD.35, CDR, TAP 3, ISUP, BSSAP, MAP, Diameter packets) straight from the switch – and effectively combine these data to uncover fraud.
Looking at more data in real-time across multiple sources or silos enables better fraud detection.
All of this data is stored in a an Cloudera enterprise data hub, where you can combine, Process, Analyze all this data to detect anomalies. The Argyle Solution sits on top – tightly integrated with Cloudera – extensively utilize Impala and also Sentry for security.
Argyle Data Solution- The first is the Fraud analytics application – used for real-time fraud detection, the second is around profit threat or SLA analytics (important because most fraud happens in roaming and there is an SLA between Service providers when a subscriber is roaming) and the third one is Forensic Analytics – this basically enables you look at an attack and see if it is an individual doing the damage or if there is a crime ring involved. The application lets you detect crime rings in a very intuitive and visual fashion and it has the ability to show 1,2,3or 4 degrees of separation (like in LinkedIN) to discover crime rings.
Real- Time Machine Learning & Anomaly Detection: As entries are stored, probabilities are generated by comparing the new entries with historic patterns in real-time enabling quicker fraud detection
Real- Time Analytics: Machine learning driven threat scoring prioritizes fraud threat attacks, allowing analysts to effectively use their time inspecting real threats. Real-time fraud alerts are sent based on anomalies detected by the machine learning system
Real- Time Threat Visualization: Platform combines massive amounts of roaming data, real-time network data, and business data into one simple easy-to-use graph analysis dashboard – makes fraud detection easy & obvious.
This is where Hadoop & machine Learning really excels in– You Need a Big Data platform to look at this scale of data in real-time and drive Machine learning & real-time analytics on this data.
Cloudera has teamed up with Argyle Data to deliver a next generation, native-Hadoop, real-time, fraud analytics platform that is tailored for today’s Communication Service Providers. This enables is CSPs to take advantage of all the data they have at their disposal to easily and effectively identify fraud and revenue threats in real-time.
Real-Time Data Ingest: we can look at data from a multitude of sources including - mobile call packets, fixed call packets, VoIP and Data packets (including TD.35, CDR, TAP 3, ISUP, BSSAP, MAP, Diameter packets) straight from the switch – and effectively combine these data to uncover fraud.
Looking at more data in real-time across multiple sources or silos enables better fraud detection.
All of this data is stored in a an Cloudera enterprise data hub, where you can combine, Process, Analyze all this data to detect anomalies. The Argyle Solution sits on top – tightly integrated with Cloudera – extensively utilize Impala and also Sentry for security.
Argyle Data Solution- The first is the Fraud analytics application – used for real-time fraud detection, the second is around profit threat or SLA analytics (important because most fraud happens in roaming and there is an SLA between Service providers when a subscriber is roaming) and the third one is Forensic Analytics – this basically enables you look at an attack and see if it is an individual doing the damage or if there is a crime ring involved. The application lets you detect crime rings in a very intuitive and visual fashion and it has the ability to show 1,2,3or 4 degrees of separation (like in LinkedIN) to discover crime rings.
Real- Time Machine Learning & Anomaly Detection: As entries are stored, probabilities are generated by comparing the new entries with historic patterns in real-time enabling quicker fraud detection
Real- Time Analytics: Machine learning driven threat scoring prioritizes fraud threat attacks, allowing analysts to effectively use their time inspecting real threats. Real-time fraud alerts are sent based on anomalies detected by the machine learning system
Real- Time Threat Visualization: Platform combines massive amounts of roaming data, real-time network data, and business data into one simple easy-to-use graph analysis dashboard – makes fraud detection easy & obvious.
This is where Hadoop & machine Learning really excels in– You Need a Big Data platform to look at this scale of data in real-time and drive Machine learning & real-time analytics on this data.
Cloudera has teamed up with Argyle Data to deliver a next generation, native-Hadoop, real-time, fraud analytics platform that is tailored for today’s Communication Service Providers. This enables is CSPs to take advantage of all the data they have at their disposal to easily and effectively identify fraud and revenue threats in real-time.
Real-Time Data Ingest: we can look at data from a multitude of sources including - mobile call packets, fixed call packets, VoIP and Data packets (including TD.35, CDR, TAP 3, ISUP, BSSAP, MAP, Diameter packets) straight from the switch – and effectively combine these data to uncover fraud.
Looking at more data in real-time across multiple sources or silos enables better fraud detection.
All of this data is stored in a an Cloudera enterprise data hub, where you can combine, Process, Analyze all this data to detect anomalies. The Argyle Solution sits on top – tightly integrated with Cloudera – extensively utilize Impala and also Sentry for security.
Argyle Data Solution- The first is the Fraud analytics application – used for real-time fraud detection, the second is around profit threat or SLA analytics (important because most fraud happens in roaming and there is an SLA between Service providers when a subscriber is roaming) and the third one is Forensic Analytics – this basically enables you look at an attack and see if it is an individual doing the damage or if there is a crime ring involved. The application lets you detect crime rings in a very intuitive and visual fashion and it has the ability to show 1,2,3or 4 degrees of separation (like in LinkedIN) to discover crime rings.
Real- Time Machine Learning & Anomaly Detection: As entries are stored, probabilities are generated by comparing the new entries with historic patterns in real-time enabling quicker fraud detection
Real- Time Analytics: Machine learning driven threat scoring prioritizes fraud threat attacks, allowing analysts to effectively use their time inspecting real threats. Real-time fraud alerts are sent based on anomalies detected by the machine learning system
Real- Time Threat Visualization: Platform combines massive amounts of roaming data, real-time network data, and business data into one simple easy-to-use graph analysis dashboard – makes fraud detection easy & obvious.
A live dashboard showing Roaming Fraud in Colombia