INTRODUCTION
While there have been several delinquencies in the responds of loan applicants, The traditional
methods of making loan decision is gradually fading out as predictive analytics (application
scoring, behavioral scoring and collection scoring) are gradually replacing traditional way ants.

In light of this, I want to demonstrate how to predict whether a loan applicant will default or not
using: SAS Enterprise Miner.

THE MODELLING STRATEGY
THE PROJECT PROCESS

1. BUSINESS OBJECTIVE/UNDERSTANDING
A Micro finance bank Institution, who offers loan credits to its customers using traditional
scoring method. In recent years, the company has given several applicants loans and many of
these accepted applicants have defaulted on their loans. Based on the datasets collected from the
past applicant, the institution wants me to build a model to predict whether an applicant will
default using Predictive Analytics

2. DATA UNDERSTANDING
Data Source: The data used contained 13 variables where 12 variables are the predictor such
as geographic, demographic and financial variables, the target variable indicate whether a
customer defaulted on the loan. The dataset had earlier been converted to a sas dataset, so the
data node was dragged into the workflow from the “data source”

Data Visualization: In order to understand the data very well, I used “graph explore” so as
to investigate each variables across each records some descriptive analysis ( mean, median,
Normal distribution, check for missing values).

3. DATA PREPARATION

Data Partitioning: “Data partition” node was used to split the data into 3 different smaller set
in the following ratio 40:30:30 . The training sample (30%) was used to build the various
models, validation sample (30%) was used to fine tune the model built and a test sample
(remaining 30%) was used for final assessment

Data Transformation: This includes the data preparation process such as dealing with missing
value, removing non-normality and applying variable reduction techniques for some models to
perform very well, all these were performed using “Transform Variables”“impute” “Variable
selection” nodes

4. MODELLING
ILLUSTRATION OF MODELS

Models: To stage a very effective model, I decided to make use of four data mining techniques;
“Regression” “Neural Network” and “Decision Tree” nodes

Regression with “imput”: I consider modeling directly after fixing the missing values problem
based on “imputed dataset” so as to improve the performance of the regression model

Regression2 with “interactive binning”: I considered building another regression model based
on “transformed variable” and “interactive Binning” this is expected to have a better
performance compared to the first regression model.

Decision Tree: Tree models will handle missing values differently than regression models, that’s
why its connected directly to “data partition” node directly Model Comparison

Neural Network: The final method considered was “Neural network” so as to compare how well
a neural network model compares to the regression and the decision tree model

5. MODEL EVALUATION

Using “Model Comparison node” The outcome of each of the models were compared in terms of
accuracy and prediction power in order to select the one with the best performance. The result
showed that Regression2 had the best performance with more 90% accuracy in terms of
classification rate

Note: some other factors aside from classification accuracy were considered before arriving at
the best performing model. Such as lift curve graphs, confusion matrix etc.

6. MODEL DEPLOYMENT FOR PREDICTION

Note: Data Mining project is not yet complete until the model built is deployed in an operational
environment. Therefore in order to predict the applicants that will default loan payments, the
model built was applied on a new datasets with similar variables.

The "Score" node scores the new datasets, while the "Reporter" node gives a full report of the
whole process and result in a pdf format

CONCLUSION AND RECOMMENDATION
This model will be able to identify the right applicant, thereby minimizing the likelihood of loan
delinquencies among the recipient of loans, because the applicant that will be granted loan will
be based on predicted cut-offs by the new score card.

Thankew score card.

Thanks for reading through…
Oladapo Ifeoluwa(2348067096726)
ifeoluwa.oladapo@gmail.com

https://www./model-built-predict-whether-loan-applicant-default-david-ifeoluwa?trk=hp-feed-article-title-publish

This is quite a long piece. Hope some people learn from it.