There are currently a lot of ways for credit scoring. The most popular one is the FICO score, and its variants. For my masters thesis, I would like to work on making my own credit scoring system using machine learning. The idea would be to obtain some real life data, and evaluate the credit scores, not necessarily in the 300-850 range as in the FICO score. What are some good resources to understand how to go about doing the same? Any new ideas are appreciated! Also, what are some places I could get free data (or not so expensive data) to build my model?

  • $\begingroup$ This is too broad and depends very much on what you know. I recommend you talk to your advisor. $\endgroup$
    – Bob Jansen
    Commented Sep 1, 2016 at 7:25
  • $\begingroup$ My advisor has asked me to work on precisely what I mentioned in the question. I have to build a credit scoring model using machine learning techniques. Any kind of new ideas or good resources on the topic would be very useful for research purposes. $\endgroup$ Commented Sep 1, 2016 at 8:11

1 Answer 1


One excellent resource is to try Kaggle and to examine some of the competitions, some of which are specifically on the application of machine learning to credit scoring.


You wil see that the winning solution is made public, including source code and output.


There are other problems in addition to this one so you should spend some time looking around. Here is another.


The preferred ML libraries are either in R or increasingly it seems that Python's Scikit learn is becoming very popular.

Also note that there are a number of p2p loan platforms in the US (and now in the UK) that provide some loan data for such analysis. Google Prosper and Lending Club. One final point is that if there is a data field with high predictivity, the p2p providers may prefer to keep it proprietary. As a result it may be hard to find models for these loans with good AUC statistics.

Stanford University also runs an ML course that covers credit scoring in the student projects submitted. Look here.


I hope that is enough to get you started.


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