In this project, your task is to help CredX identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of your project.

In some cases, you will find that all the variables in the credit bureau data are zero and credit card utilisation is missing. These represent cases in which there is a no-hit in the credit bureau. You will also find cases with missing credit card utilisation. These are the cases in which the applicant does not have any other credit card.

Create a master file with all the relevant variables and perform the necessary data quality checks and cleaning. In credit risk analytics, the weight of evidence (WOE) (and, equivalently, information value analysis) is often used to identify the important variables. Apart from assessing variable importance, WOE is also used to impute missing values from the data. You’ll note that some variables contain a significant number of missing values. Replace the actual values of all the variables by the corresponding WOE value and store the data in a separate file (e.g. woe_data) for further analysis. 

You need to assess and explain the potential financial benefit of your project to the bank's management. From a P&L perspective, identify the metrics you are trying to optimise, explain (in simple terms) how the analysis and the model work, and share the results of the model. Finally, assess the financial benefit of the model and report the following:

1. The implications of using the model for auto-approval or rejection, i.e., how many applicants on an average would the model automatically approve or reject

2. The potential credit loss avoided with the help of the model

3. Assumptions based on which the model was built 

Make appropriate assumptions about the numbers wherever needed (e.g., the potential average credit loss per default, etc.). Present your analysis and recommendations in a PowerPoint presentation.