Tuesday Jan 01, 2013

Turkcell Combats Pre-Paid Calling Card Fraud Using In-Database Oracle Advanced Analytics

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]

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.

Challenges

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

Solutions

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

Why Oracle

“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.

Partner

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.

Resources

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Thursday May 10, 2012

Oracle Virtual SQL Developer Days DB May 15th - Session #3: 1Hr. Predictive Analytics and Data Mining Made Easy!

All,

Oracle Data Mining's SQL Developer based ODM'r GUI + ODM is being featured in this upcoming Virtual SQL Developer Day online event next Tuesday, May 15th.  Several thousand people have already registered and registration is still growing.  We recorded and uploaded presentations/demos and then anyone can view them "on demand", but at the specified date/time per the SQL DD event agenda.  Anyone can also download a complete 11gR2 Database w/ SQL Developer 3.1 & Oracle Data Miner GUI extension VM installation for the Hands-on Labs and follow our 4 ODM Oracle by Examples e-training.  We moderators monitor the online chat and answer questions. 
Session #3: 1Hr. Predictive Analytics and Data Mining Made Easy!
Oracle Data Mining, a component of the Oracle Advanced Analytics database option, embeds powerful data mining algorithms in the SQL kernel of the Oracle Database for problems such as customer churn, predicting customer behavior, up-sell and cross-sell, detecting fraud, market basket analysis (e.g. beer & diapers), customer profiling and customer loyalty. Oracle Data Miner, SQL Developer 3.1 extension, provides data analysts a “workflow” paradigm to build analytical methodologies to explore data and build, evaluate and apply data mining models—all while keeping the data inside the Oracle Database. This workshop will teach the student the basics of getting started using Oracle Data Mining.
We're also included in the June 7th physical event in NYC and future virtual and physical events.  Great event(s) and great "viz" for OAA/ODM.

 Charlie

Monday Jan 18, 2010

Fraud and Anomaly Detection Made Simple

Here is a quick and simple application for fraud and anomaly detection.  To replicate this on your own computer, download and install the Oracle Database 11g Release 1 or 2.  (See http://www.oracle.com/technology/products/bi/odm/odm_education.html for more information).  This small application uses the Automatic Data Preparation (ADP) feature that we added in Oracle Data Mining 11g.  Click here to download the CLAIMS data table.  [Download the .7z file and save it somwhere, unzip to a .csv file and then use SQL Developer data import wizard to import the claims.csv file into a table in the Oracle Database.]


First, we instantiate the ODM settings table to override the defaults.  The default value for Classification data mining function is to use our Naive Bayes algorithm, but since this is a different problem, looking for anomalous records amongst a larger data population, we want to change that to SUPPORT_VECTOR_MACHINES.  Also, as the 1-Class SVM does not rely on a Target field, we have to change that parameter to "null".  See http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/anomalies.htm for detailed Documentation on ODM's anomaly detection.

drop table CLAIMS_SET;

exec dbms_data_mining.drop_model('CLAIMSMODEL');

create table CLAIMS_SET (setting_name varchar2(30), setting_value varchar2(4000));

insert into CLAIMS_SET values ('ALGO_NAME','ALGO_SUPPORT_VECTOR_MACHINES');

insert into CLAIMS_SET values ('PREP_AUTO','ON');

commit;


Then, we run the dbms_data_mining.create_model function and let the in-database Oracle Data Mining algorithm run through the data, find patterns and relationships within the CLAIMS data, and infer a CLAIMS data mining model from the data.  

begin

dbms_data_mining.create_model('CLAIMSMODEL', 'CLASSIFICATION',

'CLAIMS', 'POLICYNUMBER', null, 'CLAIMS_SET');

end;

/


After that, we can use the CLAIMS data mining model to "score" all customer auto insurance policies, sort them by our prediction_probability and select the top 5 most unusual claims.  

-- Top 5 most suspicious fraud policy holder claims

select * from

(select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud,

rank() over (order by prob_fraud desc) rnk from

(select POLICYNUMBER, prediction_probability(CLAIMSMODEL, '0' using *) prob_fraud

from CLAIMS

where PASTNUMBEROFCLAIMS in ('2 to 4', 'more than 4')))

where rnk <= 5

order by percent_fraud desc;


Leave these results inside the database and you can create powerful dashboards using Oracle Business Intelligence EE (or any reporting or dashboard tool that can query the Oracle Database) that multiple ODM's probability of the record being anomalous times (x) the dollar amount of the claim, and then use stoplight color coding (red, orange, yellow) to flag only the more suspicious claims.  Very automated, very easy, and all inside the Oracle Database! <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-46756583-1']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script>

Powerful, Yet Simple: In-Database SQL Data Mining Functions

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