# Machine Assistance for Credit Card Approval? Random Wheel can Recommend and
Explain

Anupam Khan  Department of Computer Science and Engineering, Indian Institute
of Technology Kharagpur, West Bengal 721302, India<EMAIL_ADDRESS>and
Soumya K. Ghosh  Department of Computer Science and Engineering, Indian
Institute of Technology Kharagpur, West Bengal 721302, India
<EMAIL_ADDRESS>
###### Abstract.

Approval of credit card application is one of the censorious business decision
the bankers are usually taking regularly. The growing number of new card
applications and the enormous outstanding amount of credit card bills during
the recent pandemic make this even more challenging nowadays. Some of the
previous studies suggest the usage of machine intelligence for automating the
approval process to mitigate this challenge. However, the effectiveness of
such automation may depend on the richness of the training dataset and model
efficiency. We have recently developed a novel classifier named random wheel
which provides a more interpretable output. In this work, we have used an
enhanced version of random wheel to facilitate a trustworthy recommendation
for credit card approval process. It not only produces more accurate and
precise recommendation but also provides an interpretable confidence measure.
Besides, it explains the machine recommendation for each credit card
application as well. The availability of recommendation confidence and
explanation could bring more trust in the machine provided intelligence which
in turn can enhance the efficiency of the credit card approval process.

###### Key words and phrases:

Credit card approval, predictive modelling, explainable AI, classification

* Corresponding author

##  1\. Introduction

In August 2019, The Indian Express reported nearly 24.4% and 71% increase in
credit card outstanding amount in India as compared to 2018 and 2017
respectively [1]. The Nilson Report [2] recently found a similar observation
in the United States where 24% of total credit card purchase in 2020 were
outstanding. Although the percentage declined from 28%, as observed in 2019,
it amounts to 866 billion dollars in total. Surprisingly, this value is more
than the GDP of many countries like Saudi Arabia, Turkey, Switzerland, Poland,
Thailand, Sweden, Belgium etc. [3]. Credit cards obtained through fraudulent
applications may contribute a considerable share to this outstanding amount.
Approval of credit card is, in fact, one of the critical business decision the
bankers are taking everyday. Although their experience makes them proficient
in this job, it is not possible always to take correct decisions. The
magnitude of correctness could decrease with the increasing volume of
applications to be processed daily. Interestingly, the recent pandemic has
further increased the volume of digital transactions and it could magnify the
demand of new credit card applications to a considerable extent [4]. The use
of machine intelligence may help in this regard.

Figure 1. Overview of the proposed intelligent recommendation system

Several studies [5, 6, 7] in the literature suggest using machine intelligence
for automating the credit card approval process. However, such automation may
not be effective enough in a real banking environment. It is important to
mention here that machines are not born intelligent [8]. The supervised
learning algorithms generally train them to be intelligent using the knowledge
extracted from historical data. The machines are therefore highly probable to
be biased with the historical data and learning algorithms [9]. The bias could
make a machine incapable of handling adverse situations not trained earlier.
In contrast, a human being can manage such a situation by either using his/her
skill or collaborating with others. So, should we use machine intelligence for
the automated approval of credit cards? We believe that, instead of complete
automation, machine intelligence can be used to assist human being in the
credit card approval process.

Existing machine learning approaches generally assist the decision-making
process by predicting or recommending the output of an observation. However,
it is quite often reported in literature [10, 11, 12, 13] that the end-users
are sceptical about the trustworthiness of such recommendation. It may be more
prevalent in sensitive areas like finance, healthcare etc. Significantly, it
is not possible for a machine to correctly recommend the approval of all
credit card applications. Even if a machine is tested to be sufficiently
accurate, unexpected behaviour could be possible in a real banking
environment. The availability of recommendation confidence can help in such
circumstances. Low confidence can alert the end-user about possible false
recommendation. Another potential reason behind the lack of trustworthiness is
the black-box nature of intelligent algorithms available nowadays [14, 15,
16]. Researchers have used various state-of-the-art classification approaches
like decision tree [17], support vector machine [18, 19], artificial neural
network [20, 21], genetic programming [7, 22, 23], deep learning [24, 25, 26]
etc. for predicting credit card approvals. In most cases, these machines fail
to explain why a certain credit card application is recommended to be approved
or rejected. At least we could not find any study which explains the machine
recommendation in literature.

Here, we present a completely different perspective of machine intelligence
for credit card approval. Achieving an acceptable recommendation accuracy is
not the only objective of this study. It rather aims to present a more
intelligent and trustworthy recommendation approach. Figure 1 graphically
outlines the broad objective of the proposed intelligent recommender system
for this purpose. Although various state-of-the-art classifiers could achieve
significant accuracy, extracting recommendation confidence and explanation
from these classifiers is a research challenge. We have recently proposed a
novel classifier, random wheel [27], that not only provides such confidence
but also has the potential of explaining the classification. However, it works
with categorical data only. The dataset used here for validating our methods
contains both categorical and numerical attributes. In this work, we have
therefore enhanced the random wheel to make it compatible with the mixed
dataset. Significantly, this enhancement considers input data in their actual
format without converting them to either categorical or numeric form. The
enhanced random wheel turned out to be better than state-of-the-art
classifiers. Moreover, it additionally extracts the explanations of each
individual credit card approval or rejection recommendation.

##  2\. Results

This section presents the result obtained while evaluating our methods with
the Australian credit card approval dataset [28]. This widely used dataset is
publicly available in the University of California Irvine (UCI) machine
learning repository [29]. As presented in Table 1, it contains fifteen
attributes ($A01-A15$) and one class variable ($A16$). The attribute names and
their values were changed to meaningless symbols to protect the
confidentiality of the data. Nine of these attributes
($A01,A04,A05,A06,A07,A09,A10,A12,A13$) are categorical. Amongst the other six
attributes, three ($A11,A14,A15$) contains integer values and the rest three
($A02,A03,A08$) hold real numbers. A few missing values are also observed for
these attributes. The UCI repository [28] mentions this dataset as an
interesting one due to the mixture of continuous and nominal attributes with
both small and large numbers of values. Besides, the class variable contains
two values: (i) + (positive) where credit card applications are approved, and
(ii) $-$ (negative) where the applications are rejected. In this dataset,
credit card applications are approved for 44.49% cases whereas the rest 55.51%
applications are rejected.

Table 1. Summary of credit card approval dataset Description | Value
---|---
Number of Attributes | 15
Type of Attributes | Categorical, Integer, Real
Number of Instances | 690
Missing Values | Present
Class labels |  2 ($+$ / positive, $-$ / negative)
Class Distribution | positive: 44.49%, negative: 55.51%

In this work, we compare the performance of the random wheel with other
classifiers available in the literature. We have trained the model and
extracted the effectiveness metrics using 10-fold cross-validation method.
Importantly, the depth and noise fraction of the random wheel are defined as 3
and 0.5 in this study respectively. We have considered 100 trials for
measuring factor importance and recommending an observation. The following
part of this section presents the evaluation results.

###  2.1. Comparison with state-of-the-art classifiers

The random wheel is not an enhanced version of existing classifier. It rather
targets to classify an unknown observation from a completely different
perspective. We have therefore compared the effectiveness of random wheel for
credit card approval recommendation with following ten state-of-the-art
classifiers: (i) naïve Bayes [30], (ii) Bayesian network [31], (iii) logistic
regression [32], (iv) decision tree [33], (v) support vector machine [34],
(vi) k-nearest neighbour [35], (vii) artificial neural network [36], (viii)
random forest [37], (ix) deep learning [38], and (x) boosting method [39]. The
default Weka implementation of these classifiers are considered for this
comparison.

Significantly, the random wheel turns out to be marginally better than the
above state-of-the-art classifiers. As presented in Table 2, the random wheel
yields the highest accuracy, precision, F-measure, and kappa of 0.8681,
0.8763, 0.8685, and 0.7368 respectively. The random forest is the closed
competitor in this regard. Figure 2 additionally compares the effectiveness of
various classifiers graphically. All classifiers except naïve Bayes are able
to achieve more than 80% accuracy, precision, and F-measure. However, only
Bayesian network, logistic regression, decision tree, support vector machine,
random forest and random wheel report kappa measure of more than 0.7000.

Table 2. Recommendation efficiency observed in different classifiers Approach | Classifier in Weka | Code | Accuracy | Precision | F-measure | Kappa
---|---|---|---|---|---|---
Naïve bayes | NaiveBayes | NB | 0.7770 | 0.7930 | 0.7690 | 0.5340
Bayesian network | BayesNet | BN | 0.8620 | 0.8640 | 0.8610 | 0.7186
Logistic regression | Logistic | LR | 0.8520 | 0.8540 | 0.8530 | 0.7024
Decision tree | J48 | DT | 0.8610 | 0.8610 | 0.8610 | 0.7180
Support vector machine | SMO | SVM | 0.8490 | 0.8610 | 0.8500 | 0.7003
K-nearest neighbour | IBk | kNN | 0.8120 | 0.8110 | 0.8110 | 0.6178
Artificial neural network | MultilayerPerceptron | ANN | 0.8290 | 0.8290 | 0.8290 | 0.6529
Random forest | RandomForest | RF | 0.8670 | 0.8670 | 0.8670 | 0.7295
Deep learning | Dl4jMlpClassifier | DL | 0.8160 | 0.8160 | 0.8160 | 0.6268
Boosting method | AdaBoostM1 | BM | 0.8460 | 0.8470 | 0.8460 | 0.6894
Random wheel | - | RW | 0.8681* | 0.8763* | 0.8685* | 0.7368*
* highest |  |  |  |  |  |
NBBNLRDTSVMkNNANNRFDLBMRW$0.5$$0.6$$0.7$$0.8$$0.9$ClassifierAccuracyPrecisionF-
measureKappa Figure 2. Comparison of different classifiers

###  2.2. Confidence

As mentioned in [27], the random wheel provides an additional confidence
measure while classifying an unknown observation. It measures the confidence
by comparing the angular velocities of the winner and runner-up wheel. The
confidence would be higher if the winner wheel rotates much faster than the
runner-up wheel. We have compared the average confidence of correctly
recommended observations and incorrect recommendations in the credit card
approval dataset. The average confidence of correct recommendations turns out
to be almost double concerning to the incorrectly recommended cases. An
average 52.19% confidence is observed in 599 correct recommendations. In
contrast, the average confidence of 91 incorrectly recommended cases is 24.08%
only.

Figure 3 presents a heatmap of observed confidences in decreasing order to
depict the above-mentioned difference. The colour here changes from blue to
yellow with decreasing value of confidence. Figure 3a shows the observed
confidence of 91 incorrect recommendations whereas Figure 3b presents the
confidence of 599 correctly recommended observations. It is quite evident from
Figure 3b that the higher confidence is more frequent in the case of correct
classifications. In contrast, a yellow colour in the major part of Figure 3a
indicates low confidence for incorrectly classified cases.

stackColor[1][100]
/ *ı∈[]S0pt

wrong.dat[1.0]

(a)

correct.dat[1.0] S0pt [] 0

(b)

Figure 3. Observed prediction confidence for (a) incorrect recommendations,
and (b) correct recommendations

(a) Case-I

(b) Case-II

Figure 4. Sample output of credit card approval prediction using random wheel

###  2.3. Explanation

As mentioned earlier, the objective of this work is not only to achieve better
efficiency but also to provide a trustworthy explanation while recommending
the credit card approvals. The wheels in a random wheel trial rotate on the
application of multiple elementary forces. The randomly selected set of
deciding factors determines the magnitude of these elementary forces. It is
therefore quite possible to determine the contribution of each deciding factor
and in turn each attribute, on the angular velocity of the wheel.

Figure 4 shows the sample outputs provided by the random wheel while
recommending two different observations. In Case-I (refer Figure 4a), it
correctly recommends the credit card approval with 85.59% confidence. The
$A09$, $A11$, and $A15$ are the top three attributes that contribute 17.67%,
17.41%, and 9.14% in this recommendation respectively. Figure 4b presents the
second case where the random wheel wrongly recommends approving the credit
card application. However, it is not very confident about this recommendation
here. Furthermore, it recommends approving the application primarily due to
$A09$ which contributes more than one-fourth of the total. The $A11$ and $A05$
are the other two most important factors here but they together contribute
less than 15% of the total.

##  3\. Discussion and conclusion

In this work, we have presented the effectiveness of the random wheel
classifier in recommending credit card approvals. The results show that it is
capable of providing more trustworthy recommendation concerning the other
state-of-the-art classifiers available nowadays. The availability of
additional confidence measure and explanation in the random wheel could build
trust among bankers and help them in accepting the machine intelligence for
credit card approvals.

Let us discuss this in light of the objectives depicted in Figure 1. The
banker may ask a machine to tell whether s/he should approve the credit card
application. An intelligent machine should be capable of providing a statement
like “I wouldn’t recommend to approve this request”. Importantly, a random
wheel empowered machine can provide a more accurate and precise recommendation
as it shows better effectiveness than other state-of-the-art classifiers. The
banker may further ask whether the machine is sure about this recommendation.
Most of the traditional state-of-the-art classifiers would fail to answer
this. Some advanced classifiers can provide a weightage for each possible
output. For example, tree augmented naïve Bayes [40] provides separate
weightage for each class labels during recommendation. However, multiple
weights may lead to more confusion for the end-user. In contrast, the random
wheel empowered machine is capable of answering like “I am 72% confident about
my recommendation”. Such single confidence measure could be more interpretable
by the banker while approving a credit card applications.

Besides, the random wheel is also capable of explaining individual
recommendation. The traditional machine learning algorithms generally explains
the effectiveness of individual attributes by measuring the factor importance.
It indicates the global explanation of how various attributes could influence
the machine recommendation on a particular dataset. The literature has a rich
history of such global explanations. [16] rightly mentioned that we should now
focus on the local explanations of input features on the individual
recommendation. Significantly, a random wheel empowered machine is capable of
explaining individual recommendation by extracting the contribution of each
attribute on a classification task. It, therefore, provides a trustworthy
explanation, for example, like “Following are the top three reasons and their
corresponding contributions on my recommendation: (1) applicant is jobless
(25%), (2) belongs to a problematic region (14%), (3) age of the applicant is
57 (12%)”.

Availability of explanation with machine assisted recommendation could bring a
radical change in the area of artificial intelligence. It could remove the
reluctance on accepting the machine intelligence and help in growing trust
among the beneficiaries. This has brought extensive focus on the emerging area
of explainable artificial intelligence nowadays. In this work, we have
presented an approach to deal with such an explanation in the banking sector.
Such explanation can further help in other sensitive areas like healthcare
where trust in the machine assistance is very crucial. Besides, further impact
analysis of explanation availability on the banker’s decision could be an
interesting future research direction.

## Enhanced random wheel

The novel wheel based classification approach [27] was earlier proposed by us
for predicting student performance. The concept behind this approach emerged
from the rotation of multiple wheels in a bicycle race where each wheel
represents a class label. These wheels rotate on the application of different
resultant forces in multiple trials. Significantly, the random wheel
classifier determines the magnitude of the resultant forces in a trial using a
fixed set of deciding factors. These factors are the combination of different
attributes available in the dataset. In $i^{th}$ trial, this novel classifier
first chooses the $n_{i}$ (a random integer) most important deciding factors.
These $n_{i}$ factors generate different weighted elementary forces on the
wheels which causes them to rotate with different angular velocities. The
classifier aggregates all angular velocities in multiple trials to finally
classify an unknown observation.

It is important to mention here that the earlier version of the novel random
wheel classifier considered categorical attributes only. It used mean decrease
Gini coefficient, scale Gini coefficient and lift of association for measuring
factor importance, weightage and elementary force respectively. However, these
metrics are challenging to measure in presence of continuous attributes. We
have therefore adopted a different methodology to measure these metrics as the
credit card approval dataset contains both categorical and continuous
attributes. This enhanced version of the random wheel handles the mixed type
of attributes without conversion. Here we present the factor importance and
resultant force measurement methodologies in the enhanced random wheel
classifier. We additionally propose the methodology to extract the explanation
of machine recommendation in the latter part of this section.

### Factor importance

In this work, we have considered the mean decrease in the number of bins to
measure the factor importance. Let us explain the concept of bin first. Say
the training dataset of credit card approval contains ten records and the
labels of class variable are ordered as {$+,+,-,-,-,+,-,+,-,-$}. A bin is a
subset of ordered class labels where all items of a bin are the same. The bin
changes whenever the class label switches from $+$ to $-$ or vice-versa. The
above-mentioned training dataset contains six bins as the label changes on
five occasions. Here, the bins are: {{$+,+$}, {$-,-,-$}, {$+$}, {$-$}, {$+$},
{$-,-$}}.

Important factors generally take a leading role in deciding the classification
output. If plotted graphically, they are more capable of grouping the
identical class labels than that of a comparatively less important factor. It
is therefore quite obvious that the training dataset would contain fewer bins
if sorted as per the attribute values of a more important factor. The number
of bins, in contrast, can increase as well when sorted as per a noisy factor.
Significantly, an ideal factor should produce only $m$ bins in $m$-ary
classification. It is essential to mention here that some datasets could be
imbalanced in nature. These datasets themselves contain fewer bins by default.
We have therefore considered both expected bin count in the training dataset
and sorted dataset for importance measurement.

Figure 5. Methodology for measuring factor importance

Figure 5 graphically explains the enhanced mechanism of measuring factor
importance in the random wheel. In step-I, we have first measured the expected
number of bins in the training dataset. Significantly, the number of bins is
dependent on the order in which the training records are arranged. We have
therefore randomly shuffled the training dataset multiple times and measured
the average number of bins to obtain the expected bin count. Further division
by the number of records in the training dataset provides the default bin
ratio ($A$). In step-II, we have measured the expected bin count concerning
each factor separately. It begins with filtering out all training records
where missing values are observed in any attribute of the concerning factor.
Let say the factor is $k$ and it is a combination of three attributes
$\\{a_{1},a_{2},a_{3}\\}$. In order to measure the expected bin count for $k$,
the filtration process removes all records where values are missing for either
$a_{1}$ or $a_{2}$ or $a_{3}$. The next objective is to sort the filtered
dataset to obtain the expected number of bins in the sorted dataset.
Significantly, the sorting might produce a different number of bins based on
the permutation of attributes considered for sorting. For example, the number
of bins, while sorted based on $\\{a_{1},a_{2},a_{3}\\}$, would be different
than that of in the sorted dataset based on $\\{a_{2},a_{3},a_{1}\\}$.
Besides, the number of bins is dependent on the order of other attributes
which are not considered in $k$ as well. We have therefore randomly shuffled
the filtered dataset multiple times and measured the bin count for each
attribute permutation separately. The average of all bin counts provides the
expected number of bins concerning the factor $k$ here. We have finally
divided the average bin count by the number of records in the filtered dataset
to get the factor bin ratio ($B$). In step-III, the difference of default bin
ratio ($A$) and factor bin ratio ($B$) provides the factor importance measure.
It is significant to mention here that the factor importance calculated in
this process may produce negative value as well. The random wheel discards
these noisy factors as the number of bins increases in the sorted dataset in
such cases.

### Resultant Force

The initial random wheel classifier [27] combined multiple weighted elementary
forces to obtain the resultant force to be applied on a wheel. Here we present
the methodologies adopted in the enhanced version of this classifier to
measure the resultant force.

Figure 6. Methodology to obtain the neighbouring dataset

It is quite probable that an unknown observation would follow the same
behaviour as its’ neighbouring observations. The enhanced random wheel
classifier, therefore, considers the neighbouring dataset to measure the
weightage and elementary force with a mixed set of attributes. Figure 6
graphically presents an overview of the methods adopted for getting the
neighbouring dataset here. Let us consider the same factor ($k$) again which
is a combination of three attributes $\\{a_{1},a_{2},a_{3}\\}$ as mentioned
earlier. We have initially removed all records in the training dataset where
any attribute value corresponds to $a_{1}$, $a_{2}$ or $a_{3}$ is missing to
obtain the filtered dataset. The next step thereafter extracts all records
from the filtered dataset where the value of $a_{1}$, $a_{2}$ and $a_{3}$ are
either same or within a predefined range. We have considered the record as a
potential neighbour if the attribute value is the same in case of categorical
attributes and within a range for non-categorical attributes. Let us say that
the $a_{1}$ and $a_{2}$ are categorical and $a_{3}$ is non-categorical. The
values of these attributes in unknown observation are say $v_{1}$, $v_{2}$ and
$v_{3}$ respectively. We have considered all records of filtered dataset as
neighbour in this case where the following three conditions are satisfied: (i)
$value(a_{1})=v_{1}$, (ii) $value(a_{2})=v_{2}$, and (iii)
$v_{3}-0.5\sigma\leq value(a_{3})\leq v_{3}+0.5\sigma$. The $\sigma$ is the
standard deviation of the non-categorical attribute $a_{3}$ here. This
neighbouring dataset further helped us in measuring the factor weightage and
magnitude of elementary force to be applied on a particular wheel of the
trial.

(a) Weightage  (b) Elementary force

Figure 7. Methods for measuring weightage and elementary force of a factor
using the neighbouring dataset

The random wheel provides more weightage to those factors that can induce more
inequality into the dataset. As presented in Figure 7a, we have calculated the
scaled Gini coefficient observed in the neighbouring dataset to determine the
factor weightage here. Say the neighbouring dataset corresponding to the
factor $k$ contains $n_{1}$ and $n_{2}$ number of $+$ and $-$ class labels
respectively. The $h^{k}$, as presented in Eq. 1, would provide the weightage
of factor $k$ in that case.

$h^{k}=2\times\dfrac{n_{1}^{2}+n_{2}^{2}}{{({n_{1}+n_{2}})}^{2}}-1$ (1)

The elementary force ($f_{j}^{k}$) concerning to the factor $k$ indicates it’s
impact on class label $c_{j}$. The initial random wheel implementation [27]
considered the lift of association to measure this impact. We have followed
the same approach here as well. However, the enhanced version of this
classifier considers the neighbouring dataset along with the training dataset
to measure the lift. Figure 7b present an overview of this measurement. Let
say the probability of class label $c_{j}$ in the neighbouring dataset and
training dataset are $p_{j}^{k}$ and $\tilde{p}_{j}$ respectively. The
magnitude of elementary force would therefore be as follows:

$f_{j}^{k}=\dfrac{p_{j}^{k}}{\tilde{p}_{j}}$ (2)

Once all factor weightages and elementary forces concerning a particular class
label are measured in a trial, the random wheel classifier combines them and
applies the resultant force on the corresponding wheel. Eq. 3 determines the
resultant force to be applied on wheel $W_{j}$ representing class label
$c_{j}$ in $i^{th}$ trial here.

$F_{j}^{i}=\sum_{\forall{k}}{h^{k}\times f_{j}^{k}}$ (3)

### Explanation

As mentioned earlier, the enhanced random wheel classifier considers the
contribution of each attribute on the wheel rotation to explain the
recommendation. Figure 8 presents an overview of the methods adapted here for
this purpose. It starts with the creation of a list ($E$) that manages the
contribution of all attributes for a recommendation. For each attribute, it
iterates over the trials and aggregates the attribute contribution on the
winner wheel in all trials.

Figure 8. Methodology to obtain the explanation of recommendation

Let us now present a case how the enhanced random wheel classifier calculates
the contribution of a attribute, say $a_{1}$, in trial $p$. Say the trial $p$
considers four factors: {{$a_{1}$, $a_{2}$}, {$a_{2}$, $a_{3}$}, {$a_{1}$,
$a_{2}$, $a_{3}$}, {$a_{2}$}}. In order to measure the contribution of
$a_{1}$, it first filters all factors where $a_{1}$ exists. The classifier
therefore selects {{$a_{1}$, $a_{2}$}, {$a_{1}$, $a_{2}$, $a_{3}$}} in this
specific case. It then extracts the contribution of these two factors.
Significantly, both weightage and elementary force are considered for
measuring contribution here. Say, in case of the first factor ({$a_{1}$,
$a_{2}$}), the weightage and the corresponding elementary force on the winner
wheel are $h_{(a_{1},a_{2})}$ and $f_{(a_{1},a_{2})}$ respectively; whereas
these are $h_{(a_{1},a_{2},a_{3})}$ and $f_{(a_{1},a_{2},a_{3})}$ for the
second factor ({$a_{1}$, $a_{2}$, $a_{3}$}). Eq. 4 here provides the
contribution of attribute $a_{1}$ in trial $p$.

$\epsilon_{a_{1}}^{p}=\dfrac{h_{(a_{1},a_{2})}\times
f_{(a_{1},a_{2})}}{2}+\dfrac{h_{(a_{1},a_{2},a_{3})}\times
f_{(a_{1},a_{2},a_{3})}}{3}$ (4)

Here we consider that all attributes of a factor contribute uniformly. Eq. 4
therefore divides the contribution of the first factor by two and second by
three as there are two and three attributes available in them respectively.
Once the attribute contributions in all trials are measured, the classifier
finally calculates the percentage contribution for all of them and provide
explanation in descending order of their contribution.

## References

  * [1] “Credit card outstandings rise 24.4% to Rs 97,650 crore against Rs 78,499 crore a year ago”  In _The Indian Express_ , 2019  URL: https://indianexpress.com/article/business/credit-card-outstandings-rise-economy-6090625/
  * [2] “Outstanding on Credit Cards in the United States”  In _Nelson Report, Issue 1190 (January 2021)_ , 2021, pp. 5
  * [3] “GDP (current US$)”  In _World Bank_ URL: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
  * [4] “Coronavirus pandemic — Demand for credit cards, personal loans to go up, so will stress in these segments: Report”  URL: https://www.moneycontrol.com/news/business/coronavirus-pandemic-demand-for-credit-cards-personal-loans-to-go-up-so-will-stress-in-these-segments-report-5391551.html
  * [5] Z Zarnaz, D Biswas and K M A Hasan  “Credit Card Approval Prediction by Non-negative Tensor Factorization”  In _2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)_ , 2021, pp. 319–323  DOI: 10.1109/ICREST51555.2021.9331172
  * [6] Lei Duan  “Performance Evaluation and Practical Use of Supervised Data Mining Algorithms for Credit Card Approval”  In _2020 International Conference on Computing and Data Science (CDS)_ , 2020, pp. 251–254  IEEE
  * [7] Sum Sakprasat and Mark C Sinclair  “Classification rule mining for automatic credit approval using genetic programming”  In _2007 IEEE Congress on Evolutionary Computation_ , 2007, pp. 548–555  IEEE
  * [8] Kevin Warwick  “Not another look at the Turing test!”  In _International Conference on Current Trends in Theory and Practice of Computer Science_ , 2012, pp. 130–140  Springer
  * [9] Ninareh Mehrabi et al.  “A survey on bias and fairness in machine learning”  In _arXiv preprint arXiv:1908.09635_ , 2019
  * [10] Luciano Floridi  “Establishing the rules for building trustworthy AI”  In _Nature Machine Intelligence_ 1.6 Nature Publishing Group, 2019, pp. 261–262
  * [11] Ehsan Toreini et al.  “The relationship between trust in AI and trustworthy machine learning technologies”  In _Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency_ , 2020, pp. 272–283
  * [12] Raja Chatila et al.  “Trustworthy AI”  In _Reflections on Artificial Intelligence for Humanity_ Springer, 2021, pp. 13–39
  * [13] José Jiménez-Luna, Francesca Grisoni and Gisbert Schneider  “Drug discovery with explainable artificial intelligence”  In _Nature Machine Intelligence_ 2.10, 2020, pp. 573–584  DOI: 10.1038/s42256-020-00236-4
  * [14] Mattia Setzu et al.  “GLocalX-From Local to Global Explanations of Black Box AI Models”  In _Artificial Intelligence_ 294 Elsevier, 2021, pp. 103457
  * [15] Amina Adadi and Mohammed Berrada  “Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)”  In _IEEE access_ 6 IEEE, 2018, pp. 52138–52160
  * [16] Scott M Lundberg et al.  “From local explanations to global understanding with explainable AI for trees”  In _Nature Machine Intelligence_ 2.1, 2020, pp. 56–67  DOI: 10.1038/s42256-019-0138-9
  * [17] Priyanka Kumari and Smita Prava Mishra  “Analysis of credit card fraud detection using fusion classifiers”  In _Computational Intelligence in Data Mining_ Springer, 2019, pp. 111–122
  * [18] Moloud Pławiak Pawełand Abdar and U Rajendra Acharya  “Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring”  In _Applied Soft Computing_ 84 Elsevier, 2019, pp. 105740
  * [19] Ali Rodan and Hossam Faris  “Credit risk evaluation using cycle reservoir neural networks with support vector machines readout”  In _Asian Conference on Intelligent Information and Database Systems_ , 2016, pp. 595–604  Springer
  * [20] Erkan Ilgun, Ensar Mekić and Emina Mekić  “Application of Ann in Australian credit card approval”  In _European Researcher_ , 2014, pp. 334–342
  * [21] Abdullah Khan et al.  “Chicken S-BP: an efficient chicken swarm based back-propagation algorithm”  In _International Conference on Soft Computing and Data Mining_ , 2016, pp. 122–129  Springer
  * [22] Nhien-An Le-Khac et al.  “Improving fitness functions in genetic programming for classification on unbalanced credit card data”  In _European Conference on the Applications of Evolutionary Computation_ , 2016, pp. 35–45  Springer
  * [23] Sujatha Srinivasan and T Kamalakannan  “Multi criteria decision making in financial risk management with a multi-objective genetic algorithm”  In _Computational Economics_ 52.2 Springer, 2018, pp. 443–457
  * [24] Maya Markova  “Credit card approval model: An application of deep neural networks”  In _AIP Conference Proceedings_ 2321.1, 2021, pp. 30023  AIP Publishing LLC
  * [25] Md Golam Kibria and Mehmet Sevkli  “Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques”  In _International Journal of Machine Learning and Computing_ 11.4, 2021
  * [26] Victor-Emil Neagoe, Adrian-Dumitru Ciotec and George-Sorin Cucu  “Deep convolutional neural networks versus multilayer perceptron for financial prediction”  In _2018 International Conference on Communications (COMM)_ , 2018, pp. 201–206  IEEE
  * [27] Anupam Khan, Soumya K Ghosh, Durgadas Ghosh and Shubham Chattopadhyay  “Random Wheel: An Algorithm for Early Classification of Student Performance with Confidence”  In _Engineering Applications of Artificial Intelligence_ 102, 2021
  * [28] “Credit Approval Dataset”  In _University of California Irvine (UCI) Machine Learning Repository_ URL: https://archive.ics.uci.edu/ml/datasets/Credit+Approval
  * [29] Dheeru Dua and Casey Graff  “UCI Machine Learning Repository”, 2017  URL: http://archive.ics.uci.edu/ml
  * [30] George H John and Pat Langley  “Estimating Continuous Distributions in Bayesian Classifiers”  In _Eleventh Conference on Uncertainty in Artificial Intelligence_ , 1995, pp. 338–345
  * [31] David Heckerman and Michael P Wellman  “Bayesian networks”  In _Communications of the ACM_ 38.3 Association for Computing Machinery, Inc., 1995, pp. 27–31
  * [32] Saskia Le Cessie and Johannes C Van Houwelingen  “Ridge estimators in logistic regression”  In _Journal of the Royal Statistical Society: Series C (Applied Statistics)_ 41.1 Wiley Online Library, 1992, pp. 191–201
  * [33] John Quinlan  “C4.5: Programs for machine learning”  Elsevier, 2014
  * [34] Zhi-Qiang Zeng et al.  “Fast training support vector machines using parallel sequential minimal optimization”  In _2008 3rd international conference on intelligent system and knowledge engineering_ 1, 2008, pp. 997–1001  IEEE
  * [35] David W Aha, Dennis Kibler and Marc K Albert  “Instance-based learning algorithms”  In _Machine learning_ 6.1 Springer, 1991, pp. 37–66
  * [36] Frank Rosenblatt  “The perceptron: a probabilistic model for information storage and organization in the brain.”  In _Psychological review_ 65.6 American Psychological Association, 1958, pp. 386
  * [37] Leo Breiman  “Random forests”  In _Machine learning_ 45.1 Springer, 2001, pp. 5–32
  * [38] Yann LeCun, Yoshua Bengio and Geoffrey Hinton  “Deep learning”  In _Nature_ 521.7553 Nature Publishing Group, 2015, pp. 436
  * [39] Yoav Freund, Robert E Schapire and Others  “Experiments with a new boosting algorithm”  In _icml_ 96, 1996, pp. 148–156  Citeseer
  * [40] Liangxiao Jiang, Zhihua Cai, Dianhong Wang and Harry Zhang  “Improving Tree augmented Naive Bayes for class probability estimation”  In _Knowledge-Based Systems_ 26 Elsevier, 2012, pp. 239–245