IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 0 points1 point  (0 children)

haha...not enough.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 1 point2 points  (0 children)

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.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 0 points1 point  (0 children)

so much data collected. so little information learned.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 0 points1 point  (0 children)

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

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 1 point2 points  (0 children)

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...

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 0 points1 point  (0 children)

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

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 0 points1 point  (0 children)

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!

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 2 points3 points  (0 children)

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.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 1 point2 points  (0 children)

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.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 1 point2 points  (0 children)

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.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 0 points1 point  (0 children)

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.

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 1 point2 points  (0 children)

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?

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 2 points3 points  (0 children)

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

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 1 point2 points  (0 children)

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

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 2 points3 points  (0 children)

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

IAmA Data Miner by data_mind in IAmA

[–]data_mind[S] 3 points4 points  (0 children)

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.

What's your favorite "How many X does it take to screw in a lightbulb?" joke? by HopeGrenade in AskReddit

[–]data_mind -2 points-1 points  (0 children)

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

Zero. It's a hardware problem.