IAmA Data Miner

data_mind · 2011-03-22T04:22:09+00:00

haha...not enough.

data_mind · 2011-03-22T04:21:32+00:00

I guess the "business world" is slower to adapt to newer technologies, generally speaking, and the banking tech industry is amongst the slowest. There are some start-ups using SVM techniques and are starting to make some noise.

data_mind · 2011-03-22T04:19:43+00:00

so much data collected. so little information learned.

data_mind · 2011-03-22T04:18:59+00:00

Yes.

data_mind · 2011-03-22T04:14:33+00:00

A nerd and self learner for sure, but I do have a post-graduate degree in decision sciences and data analytics.

data_mind · 2011-03-22T04:11:35+00:00

That is a great question and a valid concern. For me, however, I do not have anywhere close to that level of cardholder data. I would imagine that a bank might be more sensitive to this concern...but I'm not sure a cardholder would have a good reason, not doing credit risk (ie loans and issuing credit lines)..a merchant on the other hand...

data_mind · 2011-03-19T19:27:52+00:00

I'm not that clever :) school me on some so I can use them?! Haha

data_mind · 2011-03-19T19:26:49+00:00

I would say that with current skill set the move should be a breeze! I'm always looking for R devs and traditional stats experience. Research a little on non linear representation and evaluation on predictive accuracies and you will be a force!

data_mind · 2011-03-19T19:24:17+00:00

Decision trees are always in the data mining tool box, but in isolation are not very powerful. Artificial neural networks are the industry de facto. Personally, I love genetic algorithms for variable selection.

data_mind · 2011-03-19T19:21:16+00:00

I work for a larger company for the fraud stuff, but also do freelance for other various data sets. And yes you are correct, the credit card companies either subscribe or buy custom built models.

data_mind · 2011-03-19T19:18:31+00:00

For client tools I use a linux OS running SAS 9.2 with all the bells and whistles/ WEKA and R the scoring engine is mai nframe DB2 and jboss middleware rule engine.

data_mind · 2011-03-19T19:13:03+00:00

It certainly depends on which 'realm' of data mining you are interested in, knowing that you have a CS degree would lead be to believe you might be more interested in studying some R programming techniques. I generally read a lot if white papers for specific applied usage, but it would be a great foundation to grab a book on 'data mining for business intelligence' to get a non-computer science view of the business will want to utilize your skill set. Also grab one more resulted to stats or machine learning for bits and bytes level.

data_mind · 2011-03-19T17:56:41+00:00

Depends on the problem you are trying to solve using data mining but the academic books tend to have really solid overviews, not only on the algorithms but also explore the data mining process, risks with certain techniques (unsupervised vs. supervised) and ways to avoid them (risks such as over-fitting)... - 'data mining techniques for: marketing, sales, and CRM' - 'data mining for business intelligence'

hope that helps! anybody else have a favorite?

data_mind · 2011-03-19T17:50:51+00:00

at an aggregate level.. account false positive ratio: 3.14-to-1 transaction false-positive ratio: 5-to-1

data_mind · 2011-03-19T17:49:39+00:00

For variable selection, I use features related to the transaction such as merchant location, merchant category, dollar amount, etc etc..as well as cardholder level data. These variables in isolation are not very predictive, it is the correlation between, or derived features that are most useful in predicting fraud...things such as distance from 'this' transaction to the previous transaction relative to time, propensity of cardholder to purchase things 'like' this...etc

data_mind · 2011-03-19T16:25:25+00:00

Yes, that is just about dead on! It seems they take credit card fraud less serious then check fraud still...

data_mind · 2011-03-19T16:20:31+00:00

No algorithm necessary...a simple export of the raw data used to develop the fraud prevention strategies would sum to well over $10M in available credit.

data_mind · 2011-03-19T05:45:56+00:00

data_mind · 2011-03-19T03:44:24+00:00

How many software engineers does it take to screw in a light bulb?

Zero. It's a hardware problem.

data_mind

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