Credit Evaluation Model For Banks Using Data Mining Techniques

4296 Words Jul 19th, 2015 18 Pages
Credit Evaluation Model for Banks Using Data Mining Techniques
By Sharan Brahmanapally, Bachelor of Technology

A Project submitted in Partial
Fulfillment of the Requirements
For the Degree of
Master of Science
In the field of Industrial Engineering

Advisory Committee:
Dr. Hoo Sang Ko

Graduate School
Southern Illinois University Edwardsville
August, 2015

TABLE OF CONTENTS
TABLE OF CONTENTS ii
LIST OF FIGURES iii
LIST OF TABLES iii
ABSTRACT iv
CHAPTER 1 1
INTRODUCTION 1
1.1 Introduction 1
1.1.1 Decision Process for Credit Evaluation 3
1.2 Problem Statement 4
1.3 Aim of the Project 4
1.4 Objectives of the Project 5
CHAPTER 2 6
LITERATURE REVIEW 6
2.1 Introduction 6
2.2 Theoretical Background 6
2.2.1 Decision Trees 6
2.2.2
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People who are looking to buy a new place or thing, tend to be more conservative with their budget and acquiring loans from financial institutions. The credit functionality is prime for any banking system over the tentative market conditions. The lack of general credit review system & precise methods in banks are the important reasons, why an expert support system is necessary.
This project aims to evaluate the performance and accuracy of classification models for credit evaluation. The classification models are developed based on decision trees (J48 & CART), Support Vector Machine (SVM) and Logistic Regression along with Ensemble Methods. We used a credit approval dataset from UCI repository to compare the accuracies of the various data mining techniques. All the developed models achieved more than 85% accuracy, and among them the ensemble model with bagging showed the best performance with a relatively low computational cost. The models can be used to improve the current credit evaluation process for loan approval.

CHAPTER 1
INTRODUCTION
1.1 Introduction
An advancement in technologies has enabled the banking industry to open up efficient delivery channels that can make a quick decision on credit approval without fraud.
An increase in the volume of loan application evaluation schemes that employ traditional rule based methods has been used and continues to be used by commercial banks all over the
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