Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques

Abstract

The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models. Index Terms-Artificial intelligence, machine learning, deep learning, credit risk management.

Full text


Abstract—The increased credit card defaulters have forced the
companies to think carefully before the approval of credit
applications. Credit card companies usually use their judgment
to determine whether a credit card should be issued to the
customer satisfying certain criteria. Some machine learning
algorithms have also been used to support the decision. The
main objective of this paper is to build a deep learning model
based on the UCI (University of California, Irvine) data sets,
which can support the credit card approval decision. Secondly,
the performance of the built model is compared with the other
two traditional machine learning algorithms: logistic regression
(LR) and support vector machine (SVM). Our results show that
the overall performance of our deep learning model is slightly
better than that of the other two models.
Index Terms—Artificial intelligence, machine learning, deep
learning, credit risk management.
I. INTRODUCTION
The growth of the internet has led to a significant rise in
credit card usage. It is one of the most used payment methods
these days. As the world economy increases, credit card fraud
also increasing at an alarming rate [1]. It is also evident that
credit card defaulters have also increased significantly.
Consequently, the credit card issuing institutions are
becoming meticulous in approving credit cards to customers.
In addition, the downturn of financial institutions in the USA
and Europe during the US subprime mortgage and the
European sovereign crisis has raised concerns about risk
management properly [2]. Hence, these challenges have
attracted significant attention from researchers and
practitioners. A wide range of statistical and machine learning
techniques have been developed to solve credit card related
problems (see [1]-[7]). It is found that machine learning
techniques are superior to other traditional statistical
techniques in dealing with credit scoring [8]-[11]. In
particular, deep learning is a most popular and accurate
classification technique that outperforms other machine
learning models (e.g. logistic regression (LR), linear
discriminant analysis (LDA), multiple discriminant analysis
(MDA), k-nearest neighbor (k-NN), decision trees, etc.) [12].
Deep learning is also found to be a state-of-art research area
to solve various practical problems including credit card
fraud [6]. Some of the problems for which deep learning
technique is found to be the best method to solve are
illustrated in Table I.
Manuscript received January 30, 2020; revised November 10, 2020.
Md. Golam Kibria is with the College of Business and Innovation, The
University of Toledo, Toledo, OH 43606-3390 USA and on leave from
Independent University, Bangladesh (IUB), Bangladesh (e-mail:
mkibria@rockets.utoledo.edu).
TABLE
I:
DEEP LEARNING APPLICATIONS
Area
Problem
Paper
Natural Language
Processing (NPL)
Sentiment
Analysis
Socher, Perelygin [13], Kim
[14], Wehrmann, Becker [15]
Translation
Bahdanau, Cho [16], Cho, Van
Merriënboer [17]
Question &
Answer
Feng, Xiang [18], Dong, Wei
[19]
Visual Data
Process
Image
Classification
Krizhevsky, Sutskever [20],
LeCun, Bottou [21]
Object
Detection and
Semantic
Segmentation
Girshick [22], Girshick,
Donahue [23]
Video
Processing
Tsagkatakis, Jaber [24],
Karpathy, Toderici [25]
Speech and
Audio Processing
Speech
Emotion
Recognition
(SER)
Neumann and Vu [26], Han,
Yu [27]
Speech
Enhancement
(SE)
Huang, Kim [28], Neumann
and Vu [26]
Other Problems
Social
Network
Analysis
Zhang, Zhao [29], Huang, Kim
[28]
Information
Retrieval
Deng, He [30], Shen, He [31]
Transportation
Prediction
Nie, Jiang [32], Ma, Yu [33]
Autonomous
Driving
Geiger, Lenz [34], Hadsell,
Erkan [35]
Biomedicine
Litjens, Sánchez [36], Cireşan,
Giusti [37]
Disaster
Management
Systems
Tian and Chen [38], Tian and
Chen [39]
Credit card
frauds
Niimi [6], Zhang, Han [40],
Chaudhary, Yadav [1], Saberi,
Mirtalaie [12], Dighe, Patil
[41], Pumsirirat and Yan [3],
Ong, Huang [10]
etc.
In credit card context, most studies used traditional
statistical, machine learning, and deep learning techniques to
detect credit card fraud and compared the results [1]-[3], [5]-
[7], [12], [40], [41]. However, the literature review explores
that there is a very little research done to decide whether a
customer is to be issued a credit card or not based on their
information. Therefore, this study aims to support the
decision-makers of whether a customer is to be issued a credit
card or not. This study has two objectives. First, it will build
a deep learning model based on the best parameters for the
credit card dataset. Second, a comparative study between
deep learning and traditional machine learning algorithms
(Logistic Regression and SVM) will also be conducted.
Mehmet Sevkli is with the College of Business and Innovation, The
University of Toledo, Toledo, OH 43606-3390 USA (e-mail:
mehmet.sevkli@utoledo.edu).
Application of Deep Learning for Credit Card Approval:
A Comparison with Two Machine Learning Techniques
Md. Golam Kibria and Mehmet Sevkli
International Journal of Machine Learning and Computing, Vol. 11, No. 4, July 2021
286
doi: 10.18178/ijmlc.2021.11.4.1049

II. MODELS
A. Logistic Regression Model
Logistic Regression (LR) is one of the most commonly
applied statistical techniques for credit card analysis [5], [30],
[31]. It predicts the likelihood of a result that can just have
two states (i.e. a dichotomy). The prediction depends on the
use of one or several indicators (numerical and categorical).
According to [7], it seeks the best fit parameter to determine
the probability of the binary response based on one or more
features. Based on independent variables for each credit card
application, it provides a probability that is used to classify
the application as accepted or rejected [5]. If the probability
is larger than the threshold value, it is accepted. Otherwise, it
is rejected. LR function takes as input the client
characteristics and outputs the probability of default.
  󰇛󰇜
󰇛󰇜 (1)
where in the above
 p is the probability of default
 xi is the explanatory factor i
 βi is the regression coefficient of the explanatory factor i
 n is the number of explanatory variables
For each of the existing data points, it is known whether
the client has gone into acceptance or not (i.e. p=1 or p=0).
The aim in the here is to find the coefficients β0, β1, β2, … ,
βn such that the model’s probability of default equals to the
observed probability of default.
Fig. 1. The graph of support vector regression.
B. Support Vector Machine (SVM) Model
Support vector machine (SVM) is an algorithm that learns
based on instances given and predicts [42]. For instance, an
SVM can learn to recognize fraudulent credit card activity by
examining hundreds or thousands of fraudulent and non-
fraudulent credit card activity reports. SVM was firstly
introduced by [43]. It is used as a classification and regression
tool to maximize predictive accuracy [2]. SVM is the best fit
for supervised learning where data are linearly categorized
and examined [7]. Support Vector Regression (SVR)
methods aim to approximate the following function
󰇛󰇜   (2)
by minimizing the following objective function

  
󰇛 󰇜

 (3)
where ‖w‖ is the regularization term, 󰇛 󰇜 is the loss
function and C is the trade-off between model complexity and
error on training dataset. The graphical representation of SVR
can be seen in Fig. 1. The advantage of SVR is to present
convex solution space resulting in a unique solution.
The data points are not always in a linear classification; the
kernel functions enable us to transform the nonlinear dataset
into a linear separation format. Fig. 2 shows the
transformation of a nonlinear dataset to a linear dataset by
using kernel functions.
   󰇛 
󰇜 󰇛 󰇜  

 (4)
where y is output, αi and α* are lagrange multipliers, xi is input
vector, K(xi, x) is kernel function, and ƅ is bias.
Fig. 2. The transformation of nonlinear dataset to linear dataset by using
Kernel functions.
C. Deep Learning
Deep learning (DL) is a subset of machine learning
methods based on artificial neural networks. The core concept
of deep learning is automating the extraction of features from
the data [43]. According to [44], “deep learning is a class of
machine learning algorithms that: (1) use a cascade of
multiple layers of nonlinear processing units for feature
extraction and transformation. Each successive layer uses the
output from the previous layer as input, (2) learn multiple
levels of representations that correspond to different levels of
abstraction; the levels form a hierarchy of concepts.” Deep
learning has recently drawn much attention from researchers
in the field of machine learning [6]. It is considered as a
robust algorithm for image identification and credit fraud
detection [5]. DL is a multi-layer perceptron network that
uses a stochastic gradient descent for training [7]. The deep
learning principle is similar to an ANN that has many hidden
layers. Conversely, non-deep learning feed forward neural
networks have only a single hidden layer. The given picture
shows the comparison between non-deep learning as in Fig.
3 and deep learning with hidden layers as in Fig. 4.
A sigmoid or a tahn function is applied as an activation
function in the deep learning algorithm (see 5, 6).
󰇛󰇜 󰇛    󰇜 (5)
󰇛󰇜  󰇛     󰇜 (6)
III. DATA
This study used the credit card approval dataset by UCI
Machine repository to evaluate the experimental results (see
[45]). The UCI Machine Learning Repository is considered
to be a good source of data for conducting empirical and
methodological research in deep learning. In the dataset,
arbitrary names and values were given to the attributes to
International Journal of Machine Learning and Computing, Vol. 11, No. 4, July 2021
287

maintain the confidentiality of the data. Table II illustrates the
details of the dataset.
TABLE II: ATTRBUTES INFORMATION IN DATASET
Attribute Type
A1 Nominal
A2 Continuous
A3 Continuous
A4 Nominal
A5 Nominal
A6 Nominal
A7 Nominal
A8 Continuous
A9 Nominal
A10 Nominal
A11 Continuous
A12 Nominal
A13 Nominal
A14 Continuous
A15 Continuous
A16 Dichotomous (Class Attribute)
A. Data Pre-processing
Some missing values were found in the dataset and taken
care of following the appropriate machine learning approach
to replace the missing data. All categorical attributes were
converted to binary numerical attributes. Then, all data were
normalized.
B. Data Analyzing Platform
Data were analyzed using respective machine learning
algorithms (LR, SVM, and DL) with different parameters.
The WEKA tool was used for SVM and LR while Python
programming language was developed for DL.
IV. EXPERIMENTAL DESIGN
The main purpose of this study is to build a deep neural
network based on parameters that provide the best
performance. Different configurations of DL architectures are
examined in this study by varying the number of layers and
the number of neurons in each layer to see which
configuration gives best performance on the data set. A total
of 24 different combinations are evaluated for DL in which
2-, 3-, 5-, and 7-hidden layer networks with 3, 5, 7, 16, 32 and
64 neurons are experimented with. The number of neurons is
kept the same in each layer for a single network configuration.
For instance, if it is a 5-hidden layer network with 16 neurons,
then each of the 5 hidden layers will have 16 neurons. In the
first experimentation, the following parameters of the DL
were a used-loss function: binary cross-entropy, optimizer:
adam, activation function: rectified linear units (ReLU), the
batch size for training and prediction: 15 and epochs: 50. A
sigmoid function was used in the output layer. The popular
10-fold cross-validation approach is used for model
evaluation and model selection to avoid overfitting classifiers
[46]. Tuning with a grid search in parameter space is
employed for fine-tuning the important parameters to find out
the best parameters. After several experiments, Table III
shows the best parameters used in the deep learning model:
TABLE III: PARAMETER TUNING
Parameters
Possible values Best
parameter
s
batch_size
5, 10, 15, 20, 25, 30, 50 15
epochs
10, 20, 30, 50, 75, 100, 200 100
Optimization
‘SGD’, ‘RMSprop’, and
‘Adam’ ‘RMSprop
’
Network Weight
Initialization
‘uniform’, ‘lecun_uniform’,
‘normal’, ‘zero’,
‘glorot_normal’,
‘glorot_uniform’, ‘he_normal’,
and ‘he_uniform’
‘uniform’
Activation Function
‘softmax’, ‘relu’, ‘tanh’,
‘sigmoid’ ‘relu’
neurons
1, 3, 5, 7, 10, 15, 20 5
V. PERFORMANCE EVALUATION
A. Metrics
The chosen algorithms assume the underlying fraud
detection issue as a classification problem. We have
considered the confusion matrix given in Table IV for
evaluating metrics. However, classical metrics of accuracy
and confusion matrix will not be able to capture the actual
fraud identification rate due to skewness in instances of each
class. Thus, metrics that balance the detection of both classes
have been considered.
Fig. 3. Single layer hidden neural network. Fig. 4. Deep neural network.
Based on the confusion matrix, the following classification
performance measures are used to evaluate the model
performance:
Accuracy: (TP + TN) / (TP + FP + TN + FN) (7)
Recall (or Sensitive / True positive rate): TP / (TP + FN)(8)
Precision: TP / (TP + FP) (9)
F1-measure: 2×((Precision×Recall)/(Precision+ Recall))(10)
International Journal of Machine Learning and Computing, Vol. 11, No. 4, July 2021
288

False positive rate: FP / (FP + TN) (11)
TABLE IV: CONFUSION MATRIX FOR EVALUATING CLASSIFICATION
Predicted Class
Positive Negative
Actual Class Positive TP FN
Negative FP TN
B. Experimental Results
In this paper, three algorithms namely SVM, LR, and DL
are compared with each other. The WEKA (Waikato
environment for knowledge analysis) tool is used for Support
Vector Machine (SVM) and Logistic Regression to calculate
the efficiency based on accuracy garnered from the confusion
matrix and Python programming language is developed for
Deep Learning (DL).
TABLE V: RESULTS
Classifier F1-
Measure Precision Recall False
Positive
(FP) Accuracy
SVM .863 86.80% 86.20% 12.80% 86.23%
LR .861 86.40% 86.20% 16.10% 86.23%
DL .886 87.91% 89.26% 16.00% 87.10%
Table V illustrates the experimental results. For each
classifier, F1-Measure, Precision, Recall, FP and the accuracy
are displayed. As the deep learning results depend on the
initial parameters, the algorithm was run for 5 times and the
results reported in Table V are the average results of the five
experimentations. Accuracy is the percentage of correctly
classified instances and provides a measure for the ability to
make accurate predictions on previously unseen cases. The
F1-measure can reflect the overall performance of the model.
The recall metric represents the proportion of the actual
rejected applications that have been correctly predicted, while
the precision metric denotes the proportion of the correctly
predicted rejected applications to the predicted rejected
applications. Both recall and precision are important
evaluation metrics. In addition, the false positive rate is
defined as the proportion of the applications that have been
wrongly categorized as positive (false positives).
As shown in Table V, the accuracy rate of DL is the highest
at 87.10%. However, the accuracy rates of the other two
classifiers are same at 86.23%. Moreover, the precision and
recall of DL is higher than that of SVM and LR. Recall value
is the same for both SVM and LR while precision slightly
differs from each other. The comparative results indicate that
deep learning performs better for the credit card dataset.
Specifically, based on the F1-measure, the DL achieves the
highest F1-measure score of .886, which indicates the overall
performance of the model. F1-measure value for SVM
is .863 while .861 for LR. These two algorithms produced
almost the same F1-measure scores. In respect to the false
positive rate, the SVM outperformed the other two algorithms,
12.80% for SVM, 16.10% for LR and 16% for DL. Based on
the all accuracy measures except FP in Table V, we can
conclude that the deep learning model performs slightly
better than the other two models.
VI. CONCLUSION
In this paper, we have built a deep learning model based on
the best parameters found by the grid search technique. The
built model is then applied for the credit card data set and
compared the results with logistic regression and support
vector machine models. It is concluded that the deep learning
model performed slightly better than the other two models.
LR and SVM produced almost the same results. In the future,
another experiment can be evaluated using the large dataset
to see the comparative accuracy and applicability of these
methods.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
The first author conducted the research and analyzed the
data. The second author collected data and guided the first
author throughout the research and data analysis process.
Both authors wrote the paper and approved the final version.
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Copyright © 2021 by the authors. This is an open access article distributed
under the Creative Commons Attribution License which permits unrestricted
use, distribution, and reproduction in any medium, provided the original
work is properly cited (CC BY 4.0).
Md. Golam Kibria is a Ph.D. student in the College
of Business and Innovation at The University of
Toledo, USA. He is also a lecturer (on study leave) in
the School of Business at Independent University,
Bangladesh (IUB), Bangladesh. He graduated with
BBA and MBA in management information systems at
the University of Dhaka, Bangladesh in 2011 and 2012,
respectively. His current research interest includes
machine learning, deep learning, technology adoption, e-government and
sustainable development.
Mehmet Sevkli got his Ph.D. degree in the field of
industrial engineering from Istanbul Technical
University in Istanbul, Turkey. His primary research
interests include operations research, data
analysis/business analytics, predictive and prescriptive
analytics, statistics, quantitative management,
operations management, and optimization. In
particular, he has focused on developing a new
metaheuristics approach to combinatorial optimization problems and a fuzzy
approach to multi-criteria decision-making problems. He has several papers
published in prestigious academic journals and numerous national and
international conference papers. He has supervised one PhD dissertation and
eight master theses; took part at various academic projects as project
coordinator, or project team member. He has teaching experience in diverse
fields within the industrial engineering major, namely operations research,
operations and supply chain management, facility planning, statistics and
management information systems (MIS). Dr. Mehmet Sevkli is currently a
visiting professor of Information, Operations and Technology Management,
University of Toledo.
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