By chberger on Jan 01, 2013
Turkcell İletişim Hizmetleri A.S. Successfully Combats Communications Fraud with Advanced In-Database Analytics
[Original link available on oracle.com http://www.oracle.com/us/corporate/customers/customersearch/turkcell-1-exadata-ss-1887967.html]
- Oracle Customer: Turkcell İletişim Hizmetleri A.Ş.
- Location: Istanbul, Turkey
- Industry: Communications
- Employees: 3,583
- Annual Revenue: Over $5 Billion
Turkcell İletişim Hizmetleri A.Ş. is a leading provider of mobile communications in Turkey with more than 34 million subscribers. Established in 1994, Turkcell created the first global system for a mobile communications (GSM) network in Turkey. It was the first Turkish company listed on the New York Stock Exchange.
Communications fraud, or the use of telecommunications products or services without intention to pay, is a major issue for the organization. The practice is fostered by prepaid card usage, which is growing rapidly. Anonymous network-branded prepaid cards are a tempting vehicle for money launderers, particularly since these cards can be used as cash vehicles—for example, to withdraw cash at ATMs. It is estimated that prepaid card fraud represents an average loss of US$5 per US$10,000 in transactions. For a communications company with billions of transactions, this could result in millions of dollars lost through fraud every year.
Consequently, Turkcell wanted to combat communications fraud and money laundering by introducing advanced analytical solutions to monitor key parameters of prepaid card usage and issue alerts or block fraudulent activity. This type of fraud prevention would require extremely fast analysis of the company’s one petabyte of uncompressed customer data to identify patterns and relationships, build predictive models, and apply those models to even larger data volumes to make accurate fraud predictions.
To achieve this, Turkcell deployed Oracle Exadata Database Machine X2-2 HC Full Rack, so that data analysts can build predictive antifraud models inside the Oracle Database and deploy them into Oracle Exadata for scoring, using Oracle Data Mining, a component of Oracle Advanced Analytics, leveraging Oracle Database11g technology. This enabled the company to create predictive antifraud models faster than with any other machine, as models can be built using search and query language (SQL) inside the database, and Oracle Exadata can access raw data without summarized tables, thereby achieving extremely fast analyses.
A word from Turkcell İletişim Hizmetleri A.Ş.
“Turkcell manages 100 terabytes of compressed data—or one petabyte of uncompressed raw data—on Oracle Exadata. With Oracle Data Mining, a component of the Oracle Advanced Analytics Option, we can analyze large volumes of customer data and call-data records easier and faster than with any other tool and rapidly detect and combat fraudulent phone use.” – Hasan Tonguç Yılmaz, Manager, Turkcell İletişim Hizmetleri A.Ş.
- Combat communications fraud and money laundering by introducing advanced analytical solutions to monitor prepaid card usage and alert or block suspicious activity
- Monitor numerous parameters for up to 10 billion daily call-data records and value-added service logs, including the number of accounts and cards per customer, number of card loads per day, number of account loads over time, and number of account loads on a subscriber identity module card at the same location
- Enable extremely fast sifting through huge data volumes to identify patterns and relationships, build predictive antifraud models, and apply those models to even larger data volumes to make accurate fraud predictions
- Detect fraud patterns as soon as possible and enable quick response to minimize the negative financial impact
Oracle Product and Services
- Used Oracle Exadata Database Machine X2-2 HC Full Rack to create predictive antifraud models more quickly than with previous solutions by accessing raw data without summarized tables and providing unmatched query speed, which optimizes and shortens the project design phases for creating predictive antifraud models
- Leveraged SQL for the preparation and transformation of one petabyte of uncompressed raw communications data, using Oracle Data Mining, a feature of Oracle Advanced Analytics to increase the performance of predictive antifraud models
- Deployed Oracle Data Mining models on Oracle Exadata to identify actionable information in less time than traditional methods—which would require moving large volumes of customer data to a third-party analytics software—and achieve an average gain of four hours and more, taking into consideration the absence of any system crash (as occurred in the previous environment) during data import
- Achieved extreme data analysis speed with in-database analytics performed inside Oracle Exadata, through a row-wise information search—including day, time, and duration of calls, as well as number of credit recharges on the same day or at the same location—and query language functions that enabled analysts to detect fraud patterns almost immediately
- Implemented a future-proof solution that could support rapidly growing data volumes that tend to double each year with Oracle Exadata’s massively scalable data warehouse performance
“We selected Oracle because in-database mining to support antifraud efforts will be a major focus for Turkcell in the future. With Oracle Exadata Database Machine and the analytics capabilities of Oracle Advanced Analytics, we can complete antifraud analysis for large amounts of call-data records in just a few hours. Further, we can scale the solution as needed to support rapid communications data growth,” said Hasan Tonguç Yılmaz, datawarehouse/data mining developer, Turkcell Teknoloji Araştırma ve Geliştirme A.Ş.
Oracle Partner: Turkcell Teknoloji Araştırma ve Geliştirme A.Ş.
All development and test processes were performed by Turkcell Teknoloji. The company also made significant contributions to the configuration of numerous technical analyses which are carried out regularly by Turkcell İletişim Hizmetleri's antifraud specialists.
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