Data Mining and Association Rules: Loan Application Analysis

Learning Objectives

1) Select technology components and current practices related to information systems in organizations (MBA LG 5).

2) Explore and apply data-mining techniques in a real-world setting.

This project is an INDIVIDUAL project.  Everyone must turn in their own project. Please do your own work.   

Deliverables

- Your association rules and their associated statistical evaluations in the form of an Excel workbook.

This assignment is intended to get you thinking about association rules and simple data mining techniques in a way that is 1) applicable to our class, 2) applicable to a real-world decision, and 3) interesting!  

Your assignment

For this assignment, pretend that you are a newly hired loan officer at a bank. Your new job is to approve or deny loan applications.  You must approve all or none of a loan- you cannot approve part of it.  Since you are new, you don’t have much experience.  However, your boss did give you a dataset  

The dataset included information about 425 clients that the bank has done business with in the past.  The dataset tells you some facts about the clients in the form of several independent variables (demographics, bank account info, past loan outcomes, etc.), and it also tell you whether the bank thinks these individuals are a good credit risk (indicated by the dependent variable “credit standing”).  What you would like to do is learn from these examples.  In the very near future, you will be asked to process a loan application.  Of course, since you are new, you will not have the final say- your boss or your boss’s boss will make the final call, depending on the amount of the loan.  Your boss will be watching you to see if you make stupid loans to bad clients, and also if you miss opportunities to make prudent loans to good clients.  

Fortunately, you paid attention in college, and you seem to remember a technique for finding rules that might help you make decisions.  Oh, yeah- association rules was one technique you seem to remember.  But, that’s all you remember.  So, to get brushed up, you watch the two lectures your teacher posted about association rules.

The lectures that accompany this project can be found at: 

Finally, you think you might want to try to come up with some association rules.  So, your assignment is as follows:

Come up with between at 15-20 association rules like the ones you see on the right-hand side of the spreadsheet.  Your rules can be on the right-hand side of the same worksheet (as mine are), or you may want to list them on a separate worksheet (please keep them in the same workbook/file).  

For each association rule, compute the support and confidence of each rule.

What you want to do is find a set of rules that will ID both bad and good loan risks.  So, ID some great rules, and then write a short memo for your boss.  The memo should be at least 2 paragraphs but less than 2 pages.  Assume your boss knows nothing about association rules.  DO NOT give a walk-through for each rule you discover.  Rather, start by choosing 2-3 especially informative rules and briefly discuss them as examples, and assume your boss will get the point. Wrap up your memo with a discussion of “So what?”  Why should your boss care about the rules you found, and what further action should the bank take now that you have shown them the rules you discovered? 

Submit your Excel spreadsheet and your memo (as a Word document) through Blackboard by the deadline.  

DO NOT USE MACROS in your file.  I will not open files if I am prompted that they contain macros, and you will receive a zero on the project.

I estimate that each individual will put in between 3-8 hours on this project, depending on your experience with Excel, your knowledge of financial data, your writing ability, and your creativity. 

I encourage you to get started on this project right away and do not put it off until the last few days before it is due!

Write to me or call or stop by during office hours if you have questions.