Tuesday May 29, 2012

Fraud and Anomaly Detection using Oracle Data Mining YouTube-like Video

I've created and recorded another YouTube-like presentation and "live" demos of Oracle Advanced Analytics Option, this time focusing on Fraud and Anomaly Detection using Oracle Data Mining.  [Note:  It is a large MP4 file that will open and play in place.  The sound quality is weak so you may need to turn up the volume.]

Data is your most valuable asset. It represents the entire history of your organization and its interactions with your customers.  Predictive analytics leverages data to discover patterns, relationships and to help you even make informed predictions.   Oracle Data Mining (ODM) automatically discovers relationships hidden in data.  Predictive models and insights discovered with ODM address business problems such as:  predicting customer behavior, detecting fraud, analyzing market baskets, profiling and loyalty.  Oracle Data Mining, part of the Oracle Advanced Analytics (OAA) Option to the Oracle Database EE, embeds 12 high performance data mining algorithms in the SQL kernel of the Oracle Database. This eliminates data movement, delivers scalability and maintains security. 

But, how do you find these very important needles or possibly fraudulent transactions and huge haystacks of data? Oracle Data Mining’s 1 Class Support Vector Machine algorithm is specifically designed to identify rare or anomalous records.  Oracle Data Mining's 1-Class SVM anomaly detection algorithm trains on what it believes to be considered “normal” records, build a descriptive and predictive model which can then be used to flags records that, on a multi-dimensional basis, appear to not fit in--or be different.  Combined with clustering techniques to sort transactions into more homogeneous sub-populations for more focused anomaly detection analysis and Oracle Business Intelligence, Enterprise Applications and/or real-time environments to "deploy" fraud detection, Oracle Data Mining delivers a powerful advanced analytical platform for solving important problems.  With OAA/ODM you can find suspicious expense report submissions, flag non-compliant tax submissions, fight fraud in healthcare claims and save huge amounts of money in fraudulent claims  and abuse.  

This presentation and several brief demos will show Oracle Data Mining's fraud and anomaly detection capabilities.  


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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>
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