Tuesday, April 9, 2013

Using BigML to Decide Credit Card Approval

                With the current economic situations, credit card companies are using big data analytics now more than ever. If you have ever wondered how you can either get approved or disapproved for a credit card within a few minutes if not seconds based on just a few numbers input by you then this blog is for you. Credit card companies have collected years of data and have set up certain predictive metrics to figure out if the person applying for a credit card should be approved or not. I found a great program for this type of analysis called BigML. This is another free program that is incredibly powerful.

                In order to use BigML you must first create a username and password. You will receive an email with a confirmation link once you have input your information. Then click on the confirmation link in your email and you are ready to start working with this amazing product. It has a few datasets already listed for you to learn with and one of those is, “Credit Application’s dataset.” I used that data to set up diagrams and a prediction sheet.

                Once you have input the data your screen will change to a breakdown screen of the different categories within the dataset provided. This is represented in figure 1 below. After you are on that screen you can click on view dataset and it will change to show miniature graphs of each category on the right side of the screen. This is shown in Figure 2 below.
Figure 1: Data Category Breakdown
Figure 2: Display of Miniature Category Graphs
                Once you are done with that feature you can click on the Models tab at the top. This will take you to a new screen where you will need to click on the model you wish to look at. In my opinion this is the best part of the program because it gives you an overall view of how each path can be traced to see which people would be approved or denied. This could be useful to anyone thinking about getting a credit card because they could look at the branch that they fit in and have a good idea if they would be approved for a credit card. Figure 3 below shows what this looks like with a good path of 64.61% confidence based on the answers provided on the right side of the screen. Figure 4 shows a representation of a bad applicant with a 52.30% confidence based on the information on the right of the screen.

Figure 3: Good Applicant 
Figure 4: Bad Applicant 
                Once you are done looking at this portion of the program you can click on the predictions section of the program. This section is probably the most useful for the credit card company because you input the applicant’s information and it will tell you if this applicant is a good or bad person for credit approval. This allows for quick approval for retail credit cards. Figure 5 below shows the prediction screen and some of the input sections.


Figure 5: Prediction Screen
If you are interested in trying out this program please go to:

1 comment:

  1. Couldn't find the dataset so I could closely follow, but your tute was very helpful nonetheless.