## 4 Case Study
### Data Description  
As it was mentioned before, the variables collected follow the same basis as in credit scoring models, being related to personal information, loan characteristics ([8,9]) and mobile phone usage features ([23]).  
Firstly, it is possible to access the personal information of a user from the moment a loan application is settled for the first time. There, the user needs to fill their profile to be able to interact with the service provided by this MFI. This profile includes personal information, such as date of birth and professional/educational background; it includes information related to their family (marital status and number of dependents) and property such as owning a car. Some of these variables can be updated later without creating a new profile to continue to apply for loans in this institution.  
Secondly, the loan characteristics are added based on the purpose of each application a user makes. In this case, the amount and the reason for the loan are selected as well when filling different applications.  
Finally, regarding the mobile phone usage features, two different approaches were taken for dataset A and dataset B. For dataset A, we used some features related to the installation of applications (app) and SMS received during the loan period (which is one of the differences for a credit scoring model), while on dataset B the features related to SMS were extended, not only with new variables but also collecting information from before the approval of the loan (adding to the loan period features previously on dataset A).  
In addition, in dataset A, a sample of officers was set and some personal information was collected using an online form. The variables collected were related to demographics, educational background, and experience in the company measured by the number of working days, plus two other features that will be kept unknown to protect the personal profile of each officer. This sample worked as a restriction for what loans were considered in dataset A--the loans in which the last call to the user was made by one of these officers.  
In Tables 12 and 13, in Appendixes 14 and 15, it is possible to see the variables definition and their types in more detail for dataset A and B with some variables renamed to keep the confidentiality of this MFI.  
In both Tables 2 and 3, there is a first description of some particular features to describe each datasets. This is a representation before data cleaning and it is already possible to identify where there is a lack of quality in the data, such as in the range of the variable age.  
Note that for the protection of the information integrity of this company, the values presented are not absolute but they can show how the data behaved on this study and how the output can impact this MFI.  
\begin{table}
\begin{tabular}{r r r} \hline  & **Dataset A** & **Dataset B** \\ \hline
**Number of rows** & 46,132 & 26,825 \\
**Period** & Not defined & 1 January 2019 to 1 November 2019 \\
**Percentage Paid** & 54.46\% & 55.50\% \\
**Percentage Defaulting** & 45.54\% & 44.50\% \\
**Total number of variables** & 43 & 56 \\
**Features related to officers** & 9 & 0 \\
**Features related to SMS** & 10 & 32 \\ \hline \end{tabular}
\end{table}
Table 1: Description of an overview of dataset A and B, and its main differences.