Default Prediction Model: The Significant Role of

N. A.

doi:10.34028/iajit

Article Details

Default Prediction Model: The Significant Role of

Authors:

N. A.

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.17 Issue 4, pp.57-66

Document Type:

Article

Publication Date:

1/1970

Document Language:

English

Document ID (Org No.):

2001388617

pISSN:

2309-4524

DOI:

10.34028/iajit

Keywords:

Abstract:

 For financial institutions and the banking industry, it is very crucial to have predictive models for their core 
financial activities, and especially those activities which play major roles in risk management. Predicting loan default is one of 
the critical issues that banks and financial institutions focus on, as huge revenue loss could be prevented by predicting 
customer’s ability not only to pay back, but also to be able to do that on time. Customer loan default prediction is a task of 
proactively identifying customers who are most probably to stop paying back their loans. This is usually done by dynamically 
analyzing customers’ relevant information and behaviors. This is significant so as the bank or the financial institution can 
estimate the borrowers’ risk. Many different machine learning classification models and algorithms have been used to predict 
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Random Forest) are used for prediction, comprehensive different pre-processing techniques are being applied on the dataset 
in order to gain better data through fixing some of the main data issues like missing values and imbalanced data, and three 
different feature extractions algorithms are used to enhance the accuracy and the performance. Results of the competing 
models were varied after applying data preprocessing techniques and features selections. The results were compared using F1 
accuracy measure. The best model achieved an improvement of about 40%, whilst the least performing model achieved an 
improvement of 3% only. This implies the significance and importance of data engineering (e.g., data preprocessing 
techniques and features selections) course of action in machine learning exercises.

Article Details

Default Prediction Model: The Significant Role of

Authors:

N. A.

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.17 Issue 4, pp.57-66

Document Type:

Article

Publication Date:

1/1970

Document Language:

English

Document ID (Org No.):

2001388617

pISSN:

2309-4524

DOI:

10.34028/iajit

Default Prediction

Classification

Pre-processing

Prediction

Features Selection

Generic Algorithm

PSO Algorithm

Naïve Bayes

Decision Tree

SVM

Random Forest

Banking

Risk Management

Issues

Article Details

Authors: N. A.

Publisher: Zarqa University, Jordan

Journal: The International Arab Journal of Information Technology (IAJIT) Vol.17 Issue 4, pp.57-66

Document Type: Article

Publication Date: 1/1970

Document Language: English

Document ID (Org No.): 2001388617

pISSN: 2309-4524

DOI: 10.34028/iajit

Issues

Authors:

N. A.

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.17 Issue 4, pp.57-66

Document Type:

Article

Publication Date:

1/1970

Document Language:

English

Document ID (Org No.):

2001388617

pISSN:

2309-4524

DOI:

10.34028/iajit