| Journal of Smart Sensors and Computing
Received: 18 February 2025; Revised: 30 March 2025; Accepted: 20 May 2025; Published Online: 30 May 2025.
J. Smart Sens. Comput., 2025, 1(1), 25202 | Volume 1 Issue 1 (June 2025) | DOI: https://doi.org/10.64189/ssc.25202
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)



Md. Rezaul Hossain
and Fizar Ahmed*
Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh
*Email: fizar.cse@diu.edu.bd (Fizar Ahmed)
Abstract
Loans have become an inevitable part of the contemporary financial market and thus predicting the risk
inherent in a particular loan is crucial in avoiding high levels of default and improving on the profitability of the
loans. The study fills the current research gap concerning creating and fine-tuning loan risk prediction models
by comparing the performance of different Artificial Neural Network (ANN) layers (4-layer, 5-layer, and 6-layer)
in identifying the risk attributes in loan defaults. This paper utilizes a comparative research design, using
diverse borrower attributes and a range of financial ratios. Specifically, the method like Accuracy, Precision,
Recalling is used to assess how well every configuration of ANN works on loan risk prediction. Fortnight
preliminary results do suggest that 6-layer ANN provides much higher accuracy and recall rates than 4, 5-layer
ANN neural networks. The contribution of these results is in the development of more profound and distinct
knowledge of financial analytics, along with the possibilities of the two-tiered neural network structures for
improving loan risk assessment. Additionally, the results of the study throw the light on the choice of the right
risk factors and configurations for the ANN for the practical problems of credit scoring in the financial
institutions, and opens up the directions for future research that can enhance the performance of the predictive
modeling for credit risk assessment.
Keywords: Machine learning; Artificial Neural Network; Loan identification; Loan risk analysis.
1. Introduction
Credit risk management has proved to occupy a central position in financial institutions, particularly in the
contemporary world financial scene. A particularly important process that forms the basis of credit risk
management is the prediction of loan risk, which will allow the bank to determine the probability of attracting
loan losses. In the past, loan risk assessment has to some extent been based on statistical and rule- based models
that do not accurately take into consideration the many interactions between or within loans and borrowers.
Machine learning and the Artificial Neural Networks (ANN) specifically, have enhanced and developed superior
sources of risk prediction. The solution became more accurate and malleable at the hands of ANN.
[1]
Artificial neural networks are one of the powerful computational models derived on the basis of the structure
of the human brain. It is also noticeable that they are good at modal recognition therefore making them suitable
for categorization problems like loan risk assessment. ANNs are a set of layers of complex nodes/ neurons that
can learn from data using backpropagation and gradient descent. ANN structure mainly the quantity of the
secret layers and the neurons in each of them have a greater impact on the predictive capability of the developed
model. Nonetheless, choosing the appropriate network architecture is still a problem, higher levels may provide
additional learning capability but are inclined to overfit.
[2]
Therefore, the objective of this thesis is to establish
an understanding of how the number of secret (hidden) layers influences the performance of loan risk
prediction on ANN. In particular, we will measure the performance difference of 4-layer, 5-layer, and 6-layer
of the neural network in order to determine loan defaults. In the following analysis, we aim to determine
which configuration of ANN is suitable for this task, using metrics such as network depth and the resulting
accuracy, precision, recall, etc. Furthermore, factors that include credit history, income, and employment status
of the borrowers, and their effect on each of the models will also be discussed in this study.
Based on it, the major contribution of this research will be to advance the current efforts in advancing the
loan risk predictor models and to assist the financial institutions to manage creditworthiness and loan default
risks.
1.1 Literature review
Risk assessment on loans has always been a major concern in the field of banking and finance for many years
with many papers directed toward enhancing the accuracy of the models utilizing statistical methodology
alongside artificial neural networks and other intelligent methodologies. This section discusses a historical
perspective of loan risk prediction models with ANNs and various architectures discussion.
Speaking of the methods applied earlier for loan risk prediction, it is possible to mention logistic regression,
decision trees, and discriminant analysis. According to Thomas et al., logistic regression was appropriate in this
study because it is simple and easily interpretable.
[3]
These models presume the straight-line relationship between borrower characteristics and loan performance,
while the actual data can be different. It was  Z-score model, used for corporate bankruptcy
prediction, that provided groundwork for credit risk evaluation. However, traditional models are applied to
discrete variables as linear and independent; therefore, they cannot help much when analyzing interdependent
and nonlinear data of financial fields.
[4]
By the help of evolved machine learning, random forests, decision trees, gradient boosting machines (GBMs)
and, support vector machines (SVMs) were commonly used in predictive modeling. Malhotra and Malhotra and
Lessmann et al. established that self-regulating algorithms offered superior performance to conventional
methods in dealing with vast databases that include complicated feature interactions.
[5,6]
But deep learning has
opened up new ways in dealing with non-linear relationship of loan risks prediction. Kou et al. offered an
extensive analysis; they showed how, despite the numerous choices available, deep learning models, and ANNs
predominantly, efficiently identify even subtle relationships in the financial data. Unlike conventional
approaches, these models do not entail feature engineering often needed for popular algorithms and can
learn from data directly through multiple transformations.
[7]
Because of the non-linear mapping capability of ANN, between the input and target variable, the application of
ANNs has shown promising results in loan risk prediction. Zhang and Kanda employed a simple feedforward
ANN for credit defaults with only one hidden layer including better accurate predicting as compared to logistic
regression models. But as they pointed out, they realized how delicate the model performance might be with
network architecture concerning the number of hidden nodes and layers.
[8]
In their study, Heaton et al.
showed that Deep ANNs are capable of analyzing a large volume of values making them appropriate for
analytical use in the financial sector. But the study also highlighted some of the disadvantages which include
over fitting especially with deeper networks architectures.
[9]
Goodfellow et al., reiterated the argument of depth
when addressing the effectiveness of the network stating that while deeper networks provide better solutions,
they will also lead to greater chances of overfitting and could be very computation intensive.
[10]
ANN is ideally best suited and the overall count of hidden layers and neural units has been a subject to extensive
research. Another downside of deeper networks for Hinton et al. and LeCun et al. these models enable
sophisticated performances to be learned due to their efficiency but are computationally intensive and more
sensitive to overfitting if training data is scarce.
[11,12]
Peng, Kou and Zhou discuss how the magnitude of the
              
systems with more hidden layers fared better than the shallow ones, though more care had to be taken to
avoid overfitting than through regularization techniques including dropout and batch normalization. Another
work by Chaudhary et al. focused on the impacts of different layer ANNs on the credit scoring and discovered
rising the depth of the network helped enhancing the precision of the model but added extra training
difficulty.
[13,14]
The literature review also attests to the point of feature selection and the study of effects of different risk
factors. In their study on credit scoring using logistic regression T. Hastie, R Tibshiran, and J Friedman found
predictors including credit history, income, and employment. The findings have revealed that these risk factors
are significant and are persistent contributing factors to loan default predictions.
[15]
Subsequently, Kou et al.
further developed this by using deep learning methods to investigate the effect of the aforementioned risk
factors in non-linear models. They found out that deep ANNs contain the ability to reveal the behavior
between borrower characteristics and default rates undetectable by conventional models.
[7]
Various variants of ANNs have been assessed in several comparative researches to determine their impact on
model performance. Yu et al. worked on the comparison of shallow and deep ANNs for loan default prediction
and discovered that the loan default predicting power of deeper networks is superior as well as is the
generalizing power especially when training data overabundance is present. They however pointed out that
with the increased employment of deep architectures, there were larger computational costs and that it
required more optimum techniques and ideas.
[16]
Predicting mortgage defaults using ANNs, Zhang et al.
analyzed variable layer of the ANN model. They noted that through the analysis, that greater depth of the models
led to increased performance but with much lower rates of incremental improvement. These results imply that
even though deeper architectures improve accuracy, one must design a model that balances depth and
accuracy.
[17]
2. Methodology
This section presents the procedures and procedures of carrying out the comparative analysis of loan risk
prediction by applying 4-layer, 5-layer and 6-layer Artificial Neural Networks. The methodology is divided into
key phases: It identifies the steps starting from dataset selection and preprocessing, model architecture and
training and evaluation and the comparative analysis section.
2.1 Dataset selection
The dataset utilized for the study of Loan Risk Prediction
-Loan-
 as shown in the Fig. 1.
Data used in this study will be collected from the banking sector. The dataset contains 34 attributes
(columns) and 329414 rows. The dataset will include key features, such as:
• Loan amount
• Interest rate
• Borrower income
• Employment status
• Credit score
• Loan term
• Debt-to-income ratio
• Loan purpose
Some of these factors are well understood predictors of the likelihood of loan default in the financial sector.
Fig. 1: Flow chart of proposed diagram.
2.2 Data preprocessor
Everybody knows that data pre-processing is a critical phase of a constituent exercise in machine learning.
However, there are few other pre-processing techniques that can be used in exceptional circumstances as
follows. Enhancement of each one of input data is the main reason why data pre- processing is done. In this
task, I employed TensorFlow/Keras model which is a tremendously significant part of all the data
preprocessing steps for a classification problem. These transformations make up a set of preprocessing stages
that act on the row data before feeding them into the artificial neural network for training.
[18]

description
of the preprocessing steps encoded in the class, along with a detailed explanation of each step
and its importance for classification:
The management of the missing values, outliers and or erroneous entries. All the holes in the data will be filled
by either means or medians while very extreme values in the databases will be handled using methods such as
z-score normalization.
The code prays away from the mean and scales down the data arrays to the division by the standard deviation.
This step is crucial for standardize neural network performance.
[19]
It standardizes the pixel intensity values so
as to increase the value of the dataset mean to zero and the standard deviation to one.
The mask is binarized by using a threshold that assigns pixels to the object of interest as well as the background.
Loan amount, interest rate, the  income will be continuous data type and hence will be normalized
or standardized to bring variables to one scale and this leads to the improvement of
the neural networks.
Categorical variables like, loan purpose, and employment status are going to be encoded numerically in the
described data set, using methods like one-hot encoding. That will be further divided into the training set
at 70 % and the testing set at 30 % for accuracy of the model analysis.
2.3 Machine learning models
A living systematic neural network in the brains of animals is modeled in machine learning by an artificial neural
network. An ANN, as the abbreviation says, is made up of connected units or nodes referred to as artificial
neurons, which only metaphorically resemble real neurons. This research proposed various layer configuration
of artificial neural network model mentioned above.
[13]
The study will implement three different artificial neural
network architectures:
2.3.1 Artificial neural network 4-layer architecture
The following Fig. 2 represents the architecture of a 4-layer ANN model, which has the following parts: The
input layer consists of 10 neurons and the output layer has only one neuron. There are three hidden layers in
this form of architecture, each of the hidden layers has 32 neurons.
Fig. 2: Proposed 4-layer of ANN architecture.
2.3.2 Artificial neural network 5-layer architecture
The following Fig. 3 represents the architecture of a 5-layer ANN model, which has the following parts: The
input layer consists of 10 neurons and the output layer has only one neuron. There are three hidden layers in
this form of architecture, each of the hidden layers has 32 neurons.
Fig. 3: Proposed 5-layer of ANN architecture.
2.3.3 Artificial neural network 6-layer architecture
The next Fig. 4 below depicts a 6-layer ANN model, which has the following parts: The input layer consists of 10
neurons and the output layer has only one neuron. There are four hidden layers in this form of architecture,
each of the hidden layers has 32 neurons.
Fig. 4: Proposed 6-layer of ANN architecture.
Regarding the architecture, each kind of model will be a feedforward one with fully connected layers. The
frequency of neurons in the hidden layer will be decided based on a preliminary experiment, but they will begin
with a regular setting of neurons used in standard ANN models (32 neurons per hidden layer). The last layer of
the ANN will continue to have a single neuron with an activation function of sigmoid
as the network will be
using binary classification (default, no default). Key configuration details:
• Activation functions: ReLU (Rectified Linear Unit) will be used as a function for all hidden layers since it
reduces the vanishing gradient problem considerably.
• Loss function: Hence binary cross-entropy will be used as the loss function since the format of the task is a
binary one.
• Optimizer: In training phase, the Adam optimizer will be used since it has features of both AdaGrad and
RMSProp optimizer used for large data set and neural networks.
[19]
• Regularization techniques: More hidden layers will be added, and dropout layers will be added to lower the
opcion of overfitting, and L2 will be used to punish the complex models.
3. Experiments and results
3.1 Implementation environment
For the implementation we have used anaconda version 1.9.7,
[20]
an environment of python 3.7. Anaconda is a
standard platform for data science including many libraries of various algorithms. The other configurations are:
Processor: Intel core i5 Clock Rate: 1.7 GHz RAM: 8GB.
3.2 Dataset description
As mentioned above, we have used Bangladeshi Bank Loan dataset. Bank data of loan applications with several
attributes of the borrowers was applied in this study, including credit score, income, loan amount and purpose.
Dealing with the missing values, outliers and normalizing features for training ANN models was also done on the
preprocessed dataset obtained from the above steps. Performance of model was checked using training set (70%)
and testing set (30%). After the analysis of the 9 features, the final outpu
contains identify loan high risk or low risk.
3.3 Comparison of performance metrics result
We have calculated performance metrics for the four-layer, five-layer and six-layer artificial neural networks in the
raw dataset. The performance metrics of ANN algorithms with different layers are depicted in Table 1.
Table 1: Determining the best result amongst the various layer model.
Accuracy
Precision
Recall
F1 Score
AUC-ROC
Simple ANN
85.85%
77.44%
51.54%
61.89%
0.88
4-Layer ANN
90.64%
91.52%
63.95%
75.29%
0.93
5-Layer ANN
90.70%
90.49%
65.13%
75.74%
0.93
6-Layer ANN
90.95%
90.04%
66.81%
76.71%
0.93
In this table, we found that the 6-Layer Artificial Neural Network was given the best accuracy (90.95%).
• Accuracy: The best accuracy of 90.95%, was of the 6- layer ANN, which meant that the network was better
at predicting correct outcomes for loans among all the architectures tested. Overall, the 5-layer model was
closely followed by a percentage of 90.70%, while the smallest percentage belonged to the 4-layer model, only
90.64%. The training and validation accuracy progression over epochs, along with the corresponding loss
reduction, is illustrated in Fig. 5.
Fig. 5: Accuracy and loss curve.
• Precision: The first measure of precision stated as the ratio of successfully identified positive instances out
of the overall identified positive instances was 91.52% for the 4-layer model. The precision of the 5-layer model
was 90.49% while that of 6-layer model was 90.04%. This also shows that the shallow ones is more effective
than the deeper networks in reducing False positive cases.
• Recall: The precision of treatment which quantifies the proportion of correctly predicted positive
observations against the actual observation in the current stay was also highest for the 6-layer ANN at 66.81%
for recall. The 5- layer model gave 65.13% recall while the 4-layer model gave 63.95% recall. This implies that
deeper models were in a better position to identify loan defaults.
• F1 Score: The F1 score better if the harmonic mean of precision and recall and the highest value of 76.71%
was scored by the 6-layer model. The 5-layer model was 75.74% in F1 score and the 4-layer model scored
75.29% in F1 score. From measurements derived from the outcomes, it is evident that the 6-layer ANN presented
a good balance between precision and recall.
• AUC-ROC: The AUC-ROC was highest 0.93 and its constraint for every layer. The experiment also showed
promising results of the 6- layer model with AUC-ROC = 0.93, 5- layer model with AUC-ROC = 0.93, and the
4-Layer model had AUC-ROC of 0.93.
3.4 Correlation between features
There are many reasons for the relations between dataset features. Relations between dataset give a useful
analysis and using the relation between features we can better understand the relationships between variables
of the dataset. In the statistical field, Relation be- tween two known as correlation.
Correlation can be positive and it also can be negative. When it becomes positives then that means there was a
positive relationship between those two variables. On the other hand, when it becomes negative then that
means there was a negative relationship between those two variables. Another exceptional thing is that when
we get correlation value is zero then that means those variables are independent. The co-relation between
features is given in Fig. 6.
As far as the correlation in Fig. 6 -of- 
           in the -of- there is
corresponding increase in the incidence of loan default.
Fig. 6: Co-relation between features.
4. Conclusion and future work
The findings were corroborated through our study which showed that the 5-layer and 6-layer of ANN were more
accurate than the 4-layer network, also, in terms of accuracy, Precision, Recall, F1 score and AUC ROC. The 6-
layer model recorded the best performance in all the metrics applied a result showing that the architecture
had the best capacity to capture the complex patterns in the loan data. But the performance improvement
for network depth beyond 5 layers was not significantly promising suggesting that there is diminishing returns
in terms of prediction when network complexity increases. The chosen 5-layer ANN proved to be very effective
from the point of view of both predictiveness and computation time, showing no significant difference from
the 6-layer model and being more immunized against overlearning and less computationally demanding. This
also suggests that a 5-layer architecture is a feasible model for building real-world Apps where scalability and
performance are priorities. In future we will apply more advanced tricks in Artificial Neural Network, such as
more hybrid models, algorithm optimization would be more accurate in this case.
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Supporting Information
Not applicable.
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