# Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

Authors: Pegah Sharifi; Vipin Jain; Mehdi Arab Poshtkohi; Erfan seyyedi; Vahid Aghapour
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

Credit is one of the most significant elements in banks and financial institutions. It can also be described as unpredicted events, which mainly occur in the form of either assets or liabilities. The risk occurrence is that the facility recipients have no willingness and ability to repay their debt to the bank, which is a default that is synonymous with credit risk. Credit ratings are a way to decrease and measure credit risk and, therefore, manage it appropriately. Credit rating is an approach for estimating the features and recipients of facilities’ performance based on quantitative criteria, including the company’s financial information. The anticipated future performance allows the applicants to obtain facilities with the exact specifications. In this study, due to the need and significance of calculating the credit risk concept, a novel method based on the hybrid method of artificial neural networks and an improved version of Owl search algorithm (IOSA) and forecasting of C5 risk of decision tree credit is done. This algorithm has two major parts. The decision tree runs based on an IOSA to provide the best weighting of the neural network. The weights created along with the problem data are then given as the input to the main network, and the data are classified. The algorithm has the highest level of accuracy, 96% that is much higher than other algorithms. The results also show a precision of 0.885 and a recall of 0.83 for 618 true positive samples. The proposed method has the highest accuracy and reliability toward the other comparative methods. The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.


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## Body

## 1. Introduction

The most significant factor in economic development in every country is the correct relationship between financial and production systems [1]. Banks will play an essential role in supplying manufacturing, commercial, consumable, and even government sectors as the central part of the financial system. In Iran, the country’s financial network is responsible for financing the real sectors of an economy due to the country’s economic structure, undeveloped financial market, and other nonbank and contract networks [2]. Unfortunately, the industry has not been very booming in substantiation of its missions. At this time, government support provides a base for the continuity of activities and the survival of most banks in the country [3]. A substantial volume of fuel granted facilities and banks arrears show the lack of a proper and specialized model [4].Evaluating and measuring credit risk have a particular position among the risks that banks face in a broad range of their performance [5]. Risk reduction and control could be considered as one of the productive elements in improving the credit granting process and thus in the performance of banks and an essential role in the continued provision of facilities, the profitability, and survival of banks and financial institutions [6]. The study in risk management techniques provides a base for encountering various techniques needed for using the sort of risk identification, appropriate tools for measuring, decreasing, and managing risk accordingly.Financial institutions have faced difficulties for several reasons over the years. However, the leading cause of severe banking problems is directly linked to factors. These factors, including poor standards for granting credit to borrowers, opposite parties, poor portfolio risk management, or paying less attention to economic changes or conditions, could provide a base for the deterioration in the creditworthiness of opposites. Loans could be considered the most significant and most evident source of credit risk for most banks. However, other sources of credit risk exist throughout the bank activities, which are included in the bank and commercial offices and the top and bottom line of the balance sheet items [7].In addition to the loans, banks are progressively involved in the field of various financial instruments such as acceptance, interbank transactions, commercial finance, foreign currency transaction, prospective financial contracts, swaps, bonds, common stock, optional trades, acceptance of obligations, issuing guarantees, and settlement of transactions with credit risk (or counterparty risk). It is essential for banks to recognize, measure, monitor, and control credit risk to provide enough capital to compensate for the facing risks. Granted facilities could be considered the bank’s most significant and precious assets and could also be a solution for a substantial part of the banks’ income, but the money and capital circulation in society expose the finance to a variety of risks [8].The diversity of these risks and their intensity will face deterioration and even bankruptcy if the financial institution cannot control and manage it properly. As a result, one of the most fundamental principles of credit risk management in banks and financial institutions is the accreditation of facility applicants and providing an appropriate template for the method of payment due to the rising demand for facilities and risk in these activities. Banks can use credit risk management tools, particularly credit assessment, to make lending decisions with more confidence.Therefore, the main reason for the preliminary intention of the present study is apparent. This study provides a novel and convenient way by combining artificial neural networks, a new improved version of the Owl search algorithm, and a C5 decision tree to predict and measure banks’ credit risk and solve the problems in this direction. This method needs the implementation of novel solutions based on the subject matter and the problem complexity. The main contributions of this study could be highlighted as follows:(i)
A novel hybrid method for credit risk prediction in banks.(ii)
The method is based on artificial neural networks and an improved version of the Owl search algorithm(iii)
A C5 decision tree is also used for the method improvement.(iv)
The decision tree runs based on an IOSA to provide the best weighting of the neural network.(v)
The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.The rest of this study is organized as follows: in section2, a survey of the related works has been explained. Section 3 explains the comprehensive method of the research. In section 4, an improved version of the barn owl optimization algorithm is proposed for use as an optimization tool in the model. Section 5 defines the utilized conditions during the simulations and the dataset which is collected from German credit risk has been explained. The study discusses the results of the model in section 6, and finally, the study has been concluded in section 7.


## 2. Literature Review

In the last three decades, various estimation methods have been developed to solve this problem. However, the issue of credit risk predicting of banks remains an open issue [8]. Any work performed on this issue has diverse strengths and weaknesses. Financial professionals gain valuable information and knowledge due to the nature of credit risk and research in this field [5]. This knowledge could not be achieved in other research and areas of study [9]. As a result, great attention and desire exist for establishing financial model variation. In the process of predicting credit risk, a great deal of research has been done. To rate specific banking customers, a new method had been utilized based on fuzzy sets and particular rules in the bank [10]. A method of IT2FS has been used for scoring from intervals between 0 and 100 [11].Abdou et al. presented In demand for the facility from 487 actual data (achieved through the Islamic Bank of England), of which 336 cases have been accepted and 151 cases have been rejected [12]. An artificial multilayer perceptron neural network has been trained by using this data. Finally, the usage of the mentioned model has been achieved in essential and nonessential components of bank credit risk forecasting.Gozer et al. [13] offered a hybrid model of artificial neural networks and supported vector machines to evaluate the incapability of business units to pay debts. As primary data, 62 business units were considered, half of which could not repay the debt. Different methods, including RBF networks, multilayer perceptron, and backup vector machines, have been applied to analyze the results. According to the results, the vector-based backup method has better outcomes than other methods.Keramati et al. utilized data mining techniques for analyzing credit data [14]. In this research, a comprehensive study of all the articles on this topic affords a review article for summarizing the methods offered in assessing banks’ credit. Finally, a comparison of the existing methods is presented. In 2021, Doko et al. [15] utilized different machine-learning models for generating precise models for credit risk valuation based on the North Macedonia Central Bank. They compared the results with five machine-learning models including decision tree, logistic regression, artificial neural network, and support vector machines for categorizing the credit risk data. The studied models were then evaluated by diverse machine-learning metrics, and then a detailed credit registry data-based model was presented for credit risk prediction from the population history credit. The results indicated that the best precision was obtained by utilizing the decision tree classifier with no need for scaling.Giri et al. [16] proposed an operative rule-based classification technique for credit risk forecasting based on a metaheuristic technique, called Biogeography Based Optimization (BBO) algorithm. They modified the presented BBO algorithm using rule mining and named it locally and globally tuned biogeography-based rule-miner (LGBBO-RuleMiner). The new algorithm was then employed for the optimal rule set discovery by considering an accuracy with a high value than the dataset including the continuous and categorical attributes. The proposed methods were then validated by comparing with some different rule-miners like Decision Table, PART, OneR (1R), Conjunctive Rule, JRip, Random Tree, and J48 based on some different bio-inspired optimization algorithms based on assuming two credit risk datasets. The results showed that the proposed method outperforms the other compared algorithms in dissimilar cases. Hoǆić and Saračević [17] progressed a credit risk model to the company clients for probability prediction of default (PD). They considered 151 different companies which were the clients of an Islamic bank in Bosnia and Herzegovina. The model was developed based on logistic regression. Because of the profit-loss sharing in Islamic banks, they require for evaluating successful joint investment or client financing probability. The results showed that by globalization and growing of the Islamic financing worldwide, better tools and creditworthiness predictions are required for Islamic banks.


## 3. Research Methodology

This study aims to validate banking customers on large volumes of data by utilizing the particular feature of financial transaction and customer account information to separate the excellent customer from the bad customer before the resources allocation, granting facilities to customers [18], and to avoid the postponement of bank installments and the loss of bank resources. This algorithm is based on an improved version of the Owl search algorithm combined with C5 decision tree, and artificial neural networks. The purpose of using the Improved Owl search algorithm is to optimize search data for building a decision tree. In this study, the improved Owl search algorithm can be created from the training data. An improved Owl search algorithm (IOSA) component contains different elements such as owl agents, objective function, and the initial population. This algorithm provides a base for each customer to present one agent with multiple (housing and job, saving _ status, credit_ history, checking_ status) agents. The C5 decision tree is made for attributes determining agents. This is a uniform tree, which means each node has four children. Figure 1 shows an example of the C5 Decision Tree.Figure 1
Example of the C5 decision tree.This algorithm is used to achieve the preferable ratio between various fields of bank customers after the tree implementation. The results are required to be examined after the process of modeling. Multiple indicators assess the modeling approach’s accuracy, such as sensitivity, transparency, accuracy, and precision. The perturbation matrix could be used for calculating the accuracy of the model. This matrix could be used as a functional tool for the performance evaluation of the classification method, the detection of data, or observations of distinct categories. This research aims to conduct a model to identify bank customer behavior based on the significant, influential parameters. This model is highly comprehensive besides its execution speed and has a higher detection rate than other models. Programs are used in a database with a million records for reaching this purpose. This dataset is standard. The database is normalized using standard methods and eliminates those data that have incomplete or misplaced information. In the following stage two-step, data mining with two different algorithms is performed on this dataset. The first step, which is based on the decision tree and the IOSA, is C5 powerful algorithm and is highly capable of categorizing and verifying effectual features. Customers are divided into two classes of good and bad in the mentioned three, and impressive attributes are chosen based on the number of repetitions in the tree.The second algorithm is based on neural networks. In this study, a feedforward neural network with five hidden layers is designed. The sigmoid transfer function is utilized in the first layer of the five neurons. The part of the data is given to the network in the training section. After optimization, an Improved Owl search algorithm will regulate the network weights to fewer errors between the target and the expected output. Figure2 shows the flowchart of the suggested method.Figure 2
Flowchart of the suggested method.After achieving the coefficient values, the test data are entered. In other words, the test data could be considered a collection of one million user records, which are tended to calculate the amount of their risk. The process of entering data is one by one, and the decision tree results would be selected if the documented records were the same. Still, if it did not exist, the amount of risk is calculated by the neural network. The calculation of the neural network coefficients is updated to be more precise for future records. In other words, each test record could be converted to a training record after the result. The flowchart of the proposed algorithm is illustrated in Figure2. The next section explains the new improved version of the Barn Owl algorithm that is designed and used in this study.


## 4. Improved Barn Owl and Inspiration for Optimization

Recently, the use of bio-inspired approaches has evolved into a prominent strategy for global optimization. The primary goal of these methods is to replicate various natural occurrences [19, 20], social behaviors [21], and human contests [22], among others. Diversification and intensification are critical properties of metaheuristic techniques. Intensification looks for and chooses the best candidate points surrounding the existing best answer. Convergence capability and the capacity to escape from the local optimal point are critical components of these methods. For global search, many novel bio-inspired algorithms are suggested. Jain et al. [23] recently created a promising bio-inspired method called the owl search algorithm (OSA). The OSA is based on an idealized version of owl hunting behavior.The owl is a bird that may be found on every continent except Antarctica. Because these birds hunt at night, they are referred to as night predators. Although the owl is generally considered an infelicitous bird, it is a valuable hunter due to its ability to kill rodents. The owl’s eyes are similar to those of a human being in front of his face. On the other hand, Owls are unable to move their eyes and must instead turn their heads and necks to view their environment. The owl is an elongated owl with broad wings, vivid coloration, and a nearly square tail. It measures between 2 and 4 cm in length and has a wingspan of 1 to 2 cm, depending on the subspecies. They may be distinguished from regular owls on the fly by the motions of the waves and the feathered legs. It has a golden pea-colored back surface with little dots and a very homogeneous white ventral surface. It has black eyes and no guppers. It hunts mostly at night, sitting straight with its long legs and large head. The face and legs of the owl are densely feathered. Male and female owls are similar in appearance. However, the female owl is bigger. The species range in size from 15 cm to 76 cm.The owl has exceptional hearing and eyesight. The owl’s vision abilities are so strong that they may assist it in hunting in the dark. Additionally, some owls hunt using their acute hearing. The barn owl is the most varied species of owl. Barn owls inhabit all habitats except poles and deserts, including the alpine belt, a significant portion of Indonesia, and oceanic islands. It is a stunning nighttime hunting bird, with a medium-sized white and big feather, a short, square tail, and long legs covered in white feathers. They have an excellent hearing system for finding prey. By virtue of their vertical asymmetry of ears, barn owls have developed an auditory system with a defined physical feature. This unique feature receives sound in one ear before the other, allowing for precise prey location [24].Prey may be concealed in the dark using the hearing rather than the visual sense [25]. The owl’s brain processes the sound produced by prey in two ways: the interaural level (loudness) difference (ILD) and the interaural timing difference (ITD), which are utilized to create an auditory map of the prey’s position [23]. The owl can determine the distance to its prey based on the timing and intensity variations in the arrival of sound waves.


### 4.1. Owl search Algorithm (OSA)

As with many bio-inspired optimization methods, OSA begins with initializing a random set of populations. The population in OSA depicts owls in their natural habitat, a forest, as the solution space. Usingn owls and a d-dimensional search space, the random location of owls is stored in the following n×d matrix:(1)O=O1,1⋯O1,d⋮⋱⋮On,1⋯On,d.In this case, matrix elementOi,j denotes the ith owl’s jth variable (dimension). The following formula was used to create a uniformly dispersed initial location:(2)Oi=Ol+Ol+Ou×U0,1,where U0,1 is a uniformly distributed random integer in the range 0 to 1, and Ol and Ou are the lower and upper bounds, respectively, of the ith owl Oi in the jth dimension.The cost of locating owls in a forest is computed using a cost function and stored in the following matrix:(3)f=f1O1,1,O1,2,⋯,O1,d⋮fnOn,1,On,2,⋯,On,d.The cost of the owls’ position is exactly proportional to the strength of information acquired through ears. The best owl may achieve maximum intensity (for maximization problems) in this situation since it is closer to prey. The normalized intensity value information forith owl is used to update the location, which may be accomplished in the following ways:(4)Ii=fi−wb−w,where(5)b=maxm∈1,…,nfm,and(6)w=minm∈1,…,nfm,(7)Di=∑iOi−L2,where L denotes the location of the fittest owl’s prey.The method assumes that the forest has just one prey (global optimal). Owls fly silently to approach their prey during the hunting procedure. Theith owl’s intensity changes as follows:(8)Ci=IiDi2+Rn,where Di2 is utilized in place of 4πDi2 and Rn describes random noise to add realism to the model.Because of the movements of their prey, the owls must quietly shift their current position. In OSA, prey movement is represented probabilistically, and therefore new owl locations are determined using the following mechanism:(9)Oit+1=Oit+β×Ci×αL−Oit,ppm<0.5Oit−β×Ci×αL−Oit,ppm≥0.5,where ppm denotes the prey movement probability, and α and β denote a uniformly distributed random number in the range [0, 0.5] and β linearly declines from 1.9 to 0Indeed,β is a significant variance contributing to the development of the search space’s exploration term. It may be lowered to enhance exploitation by creating these variants utilizing the method. The OSA’s reliance on a single parameter (β) distinguishes it from other bio-inspired algorithms.


### 4.2. Improved Owl search Algorithm (IOSA)

Recently, chaos theory has increased as the influence of nonlinear dynamics on system modeling has increased. This theory can also affect optimization problems. The parameterβ is the sole random value in the standard OSA. By using random values in each iteration, the system converges prematurely. A chaos mechanism termed singer mapping was used [26, 27]. This technique converts the unknown parameter β to the following regular expression:(10)βi+1=1.077.9βi−23.3βi2+28.7βi3−13.3βi4.Lévy flight (LF) is another strategy for resolving the premature convergence issue in OSA. LF is a frequently utilized technique for achieving premature convergence in bio-inspired optimization algorithms [28]. The random walk is the primary component of this technique for handling local searches properly. The mathematical formulation of this process is as follows:(11)Lew≈w−1−τ,w=AB1/τ,σ2=Γ1+ττΓ1+τ/2sinπτ/221+τ/22/τ,where w represents the step size, τ specifies the LF index between 0 and 2 (here, τ=3/2 [29]), A∼N0,σ2 and B∼N0,σ2, and Gamma function is indicated by Γ.. The following formulation may be used to obtain new owl positions based on the described cases:(12)Oit+1=Oit+β×Ci×αL−Oit×Leδ,ppm<0.5Oit−β×Ci×αL−Oit×Leδ,ppm≥0.5..Figure3 illustrates the flowchart of the given IOSA.Figure 3
Comparison results of the accuracy based on training data.


### 4.3. The validation of the Algorithm

Four standard benchmarks were examined to verify the proposed method. The method is compared to many novel algorithms, including the emperor penguin optimization (EPO) algorithm [30], the shark smell optimization (SSO) algorithm [31], and the original OSA algorithm. Rastrigin is the first benchmark, with a dimension of 30 to 50 with constraints between −512 and 512:(13)f1x=10D+∑i=1Dxi2−10cos2πxi.Rosenbrock is the second benchmark, with a dimension of 30 to 50 with constraints between −2.045 and 2.045:(14)f2x=∑i=1D−1100xi2−xi+1+xi−12.Ackley is the third benchmark. This benchmark has a dimension between 30 and 50 with constraints between −10 and 10.(15)f3x=−20exp−0.21D∑i=1Dxi2−exp1D∑i=1Dcos2πxi+20+e.Finally, Sphere is used as the fourth benchmark. This benchmark has a dimension between 30 and 50 with constraints between −512 and 512.(16)f4x=∑i=1Dxi2.Table1 summarizes the simulation results for the given technique in comparison to others.Table 1
Comparison results of the utilized algorithms on the benchmark functions.
Benchmark functionMetricEPO [30]SSO [31]WCOOSA [23]IOSAF1AVE8.25E-87.68E-99.24E-98.36E-97.22E-10STD9.03E-85.07E-94.13E-96.22E-95.35E-10F2AVE7.227.208.295.673.91STD8.463.827.242.292.83F3AVE5.915.113.433.332.07STD2.491.942.082.901.64F4AVE3.754.191.331.282.11STD2.252.351.041.130.98The results indicate that when compared to other state-of-the-art techniques, the suggested method produces the best outcomes.


## 4.1. Owl search Algorithm (OSA)

As with many bio-inspired optimization methods, OSA begins with initializing a random set of populations. The population in OSA depicts owls in their natural habitat, a forest, as the solution space. Usingn owls and a d-dimensional search space, the random location of owls is stored in the following n×d matrix:(1)O=O1,1⋯O1,d⋮⋱⋮On,1⋯On,d.In this case, matrix elementOi,j denotes the ith owl’s jth variable (dimension). The following formula was used to create a uniformly dispersed initial location:(2)Oi=Ol+Ol+Ou×U0,1,where U0,1 is a uniformly distributed random integer in the range 0 to 1, and Ol and Ou are the lower and upper bounds, respectively, of the ith owl Oi in the jth dimension.The cost of locating owls in a forest is computed using a cost function and stored in the following matrix:(3)f=f1O1,1,O1,2,⋯,O1,d⋮fnOn,1,On,2,⋯,On,d.The cost of the owls’ position is exactly proportional to the strength of information acquired through ears. The best owl may achieve maximum intensity (for maximization problems) in this situation since it is closer to prey. The normalized intensity value information forith owl is used to update the location, which may be accomplished in the following ways:(4)Ii=fi−wb−w,where(5)b=maxm∈1,…,nfm,and(6)w=minm∈1,…,nfm,(7)Di=∑iOi−L2,where L denotes the location of the fittest owl’s prey.The method assumes that the forest has just one prey (global optimal). Owls fly silently to approach their prey during the hunting procedure. Theith owl’s intensity changes as follows:(8)Ci=IiDi2+Rn,where Di2 is utilized in place of 4πDi2 and Rn describes random noise to add realism to the model.Because of the movements of their prey, the owls must quietly shift their current position. In OSA, prey movement is represented probabilistically, and therefore new owl locations are determined using the following mechanism:(9)Oit+1=Oit+β×Ci×αL−Oit,ppm<0.5Oit−β×Ci×αL−Oit,ppm≥0.5,where ppm denotes the prey movement probability, and α and β denote a uniformly distributed random number in the range [0, 0.5] and β linearly declines from 1.9 to 0Indeed,β is a significant variance contributing to the development of the search space’s exploration term. It may be lowered to enhance exploitation by creating these variants utilizing the method. The OSA’s reliance on a single parameter (β) distinguishes it from other bio-inspired algorithms.


## 4.2. Improved Owl search Algorithm (IOSA)

Recently, chaos theory has increased as the influence of nonlinear dynamics on system modeling has increased. This theory can also affect optimization problems. The parameterβ is the sole random value in the standard OSA. By using random values in each iteration, the system converges prematurely. A chaos mechanism termed singer mapping was used [26, 27]. This technique converts the unknown parameter β to the following regular expression:(10)βi+1=1.077.9βi−23.3βi2+28.7βi3−13.3βi4.Lévy flight (LF) is another strategy for resolving the premature convergence issue in OSA. LF is a frequently utilized technique for achieving premature convergence in bio-inspired optimization algorithms [28]. The random walk is the primary component of this technique for handling local searches properly. The mathematical formulation of this process is as follows:(11)Lew≈w−1−τ,w=AB1/τ,σ2=Γ1+ττΓ1+τ/2sinπτ/221+τ/22/τ,where w represents the step size, τ specifies the LF index between 0 and 2 (here, τ=3/2 [29]), A∼N0,σ2 and B∼N0,σ2, and Gamma function is indicated by Γ.. The following formulation may be used to obtain new owl positions based on the described cases:(12)Oit+1=Oit+β×Ci×αL−Oit×Leδ,ppm<0.5Oit−β×Ci×αL−Oit×Leδ,ppm≥0.5..Figure3 illustrates the flowchart of the given IOSA.Figure 3
Comparison results of the accuracy based on training data.


## 4.3. The validation of the Algorithm

Four standard benchmarks were examined to verify the proposed method. The method is compared to many novel algorithms, including the emperor penguin optimization (EPO) algorithm [30], the shark smell optimization (SSO) algorithm [31], and the original OSA algorithm. Rastrigin is the first benchmark, with a dimension of 30 to 50 with constraints between −512 and 512:(13)f1x=10D+∑i=1Dxi2−10cos2πxi.Rosenbrock is the second benchmark, with a dimension of 30 to 50 with constraints between −2.045 and 2.045:(14)f2x=∑i=1D−1100xi2−xi+1+xi−12.Ackley is the third benchmark. This benchmark has a dimension between 30 and 50 with constraints between −10 and 10.(15)f3x=−20exp−0.21D∑i=1Dxi2−exp1D∑i=1Dcos2πxi+20+e.Finally, Sphere is used as the fourth benchmark. This benchmark has a dimension between 30 and 50 with constraints between −512 and 512.(16)f4x=∑i=1Dxi2.Table1 summarizes the simulation results for the given technique in comparison to others.Table 1
Comparison results of the utilized algorithms on the benchmark functions.
Benchmark functionMetricEPO [30]SSO [31]WCOOSA [23]IOSAF1AVE8.25E-87.68E-99.24E-98.36E-97.22E-10STD9.03E-85.07E-94.13E-96.22E-95.35E-10F2AVE7.227.208.295.673.91STD8.463.827.242.292.83F3AVE5.915.113.433.332.07STD2.491.942.082.901.64F4AVE3.754.191.331.282.11STD2.252.351.041.130.98The results indicate that when compared to other state-of-the-art techniques, the suggested method produces the best outcomes.


## 5. Simulation Conditions

In this research, all implementations have been conducted in the Windows environment using the 64-bit version of MATLAB 8.0.604 (2014b) and performed on the computation environment of a 2 GHz-Core i7 CPU, with 8 GB RAM and Windows 7 operating system. In this study, Statlog valid database (German credit risk) that is collected by Professor Hoffman has been utilized. This database has two classes of 1000 valid data and is a compromise of 24 diverse aspects. Of these 1000 data, 700 data indicate reasonable credit risk (without risk) and 300 data represent bad credit risk with (with risk). The significant aspects considered in the Statlog database are as follows:(1)
Existence or nonexistence of account with a check handle (four various modes) (A1)(2)
Monthly turnover rate (A2)(3)
Credit history (five various modes) (A3)(4)
Target (eleven various modes) (A4)(5)
The amount of credit received (A5)(6)
Savings account and its amount (five various modes) (A6)(7)
The Duration of employment by occupation (five various modes) (A7)(8)
Installment ratio in return for assets seized by the bank (A8)(9)
Gender and marital status (five various modes) (A9)(10)
Debt or previous warranty (three various modes) (A10)(11)
Residence status (A11)(12)
Assets (four various modes) (A12)(13)
Age (A13)(14)
Other installments (three various modes) (A14)(15)
Housing status (three various modes) (A15)(16)
The number of credits in the bank (A16)(17)
Job status (four various modes) (A17)(18)
The number of Guarantors (A18)(19)
The availability of landline phones (A19)(20)
Foreign employee (A20)


## 6. The Results of the Suggested Algorithm

In this research, the results of the suggested algorithm performance have been presented on Statlog data. The suggested method was compared with numerous criteria, which are as follows:


### 6.1. Accuracy

Figure3 shows that the output result accuracy could be considered one of the most critical evaluation criteria. The number of training data in this assessment is changeable, and output accuracy has been compared based on the training data. As Figure 3 illustrates, the suggested method is more accurate than the Decision Tree, and increasing the number of data adds to the result’s precision. In Figure 4, the accuracy is compared based on the test data criteria.Figure 4
Comparison results of the accuracy based on test data.Figure4 illustrates that the suggested method has higher accuracy. As the number of test data increases, the accuracy increases because, with any test data after execution, it will be added to the training data, which provides a base for the accuracy to be improved. According to Figure 4 in this research, the presented algorithm has the highest accuracy of 98% on the Statlog database, which is preferable. Based on our expectations, increasing the initial population provides a base for the algorithm’s accuracy to increase. The reason for such an idea is that in every population, each element is a probable solution. The chance of reaching a more precise response is increasing as this initial solution amplifies. It should be mentioned that the calculated accuracy is an average of 50 performances of different algorithms with the same condition.


### 6.2. Runtime criteria

The shorter the implementation time in each algorithm, the superior the above algorithm. As shown in Figure5, the implementation time of the suggested algorithm has been compared. According to Figure 5, the suggested methods have a higher speed compared to the ADTree method. Table 2 indicates the runtime comparison.Figure 5
Runtime comparison.Table 2
Runtime comparison.
Test data numberMethodIOSA-based methodDecision treeOSA-based method10023.4618.917.540042.133.131.280068.354.953.2100093.880.179.8


### 6.3. Other methods Comparison

The suggested method is compared based on Improved Owl search algorithm algorithms and artificial neural networks by using well-known Weka software with other conventional methods in machine learning. The Weka software could be considered an assortment of the most up-to-date machine learning algorithms and tools for processing data.All features of the software are available to users in the form of proper user interfaces. As a result, they can implement diverse methods on their data and select the best algorithm for this purpose. In this part, numerous tests have been performed using various learning machines, including Weka software on the primary database, the bank’s credit risk database. The proposed learning methods are AdaboostM1, Bagging, Random Forest, Rotation Forest, Multi-Layer Perception (MLP), and AD Tree.As illustrated in Table3, the suggested method accuracy is preferable to other methods on this particular database. This is noticeable by analyzing the accuracy achieved in each process and the suggested approach. These methods have been modified under the identical conditions of 10-fold cross-validation.Table 3
Accuracy of the suggested algorithm with other methods comparison.
AlgorithmAccuracy (%)Error (%)Proposed method964.0Bagging69.730.3AdoboostM169.730.3Rotation forest73.826.2Random forest75.924.1ADTree8020.0MLP72.327.7In this research, the error of provided method was less comparing other forms; as a result, it has higher accuracy. Table4 compares the performance of this algorithm and other methods.Table 4
Implementation time comparison of the suggested algorithm with other methods.
AlgorithmRun time (seconds)Proposed method80.1Bagging266.1AdoboostM164.9Rotation forest2181.9Random forest561.2ADTree93.9MLP4033.6According to the above Table, this algorithm takes less time to run than most methods and is more than the Adaboost M1 algorithm. Due to the accuracy of the measurement and the offline nature of the forecast, this issue can be ignored. Table5 illustrates a comprehensive statistical comparison between the suggested method and the methods mentioned earlier, indicating the provided method’s high accuracy.Table 5
Comparison statistical of the proposed algorithm with other methods.
AlgorithmTPEPFNTNPrecisionRecallProposed method618821381650.8850.83Bagging606951691340.8640.79AdoboostM161685218820.8780.74Rotation forest614881631370.8750.79Random forest639631681320.9120.79ADTree5961061721280.840.78MLP5621381471550.8020.79


## 6.1. Accuracy

Figure3 shows that the output result accuracy could be considered one of the most critical evaluation criteria. The number of training data in this assessment is changeable, and output accuracy has been compared based on the training data. As Figure 3 illustrates, the suggested method is more accurate than the Decision Tree, and increasing the number of data adds to the result’s precision. In Figure 4, the accuracy is compared based on the test data criteria.Figure 4
Comparison results of the accuracy based on test data.Figure4 illustrates that the suggested method has higher accuracy. As the number of test data increases, the accuracy increases because, with any test data after execution, it will be added to the training data, which provides a base for the accuracy to be improved. According to Figure 4 in this research, the presented algorithm has the highest accuracy of 98% on the Statlog database, which is preferable. Based on our expectations, increasing the initial population provides a base for the algorithm’s accuracy to increase. The reason for such an idea is that in every population, each element is a probable solution. The chance of reaching a more precise response is increasing as this initial solution amplifies. It should be mentioned that the calculated accuracy is an average of 50 performances of different algorithms with the same condition.


## 6.2. Runtime criteria

The shorter the implementation time in each algorithm, the superior the above algorithm. As shown in Figure5, the implementation time of the suggested algorithm has been compared. According to Figure 5, the suggested methods have a higher speed compared to the ADTree method. Table 2 indicates the runtime comparison.Figure 5
Runtime comparison.Table 2
Runtime comparison.
Test data numberMethodIOSA-based methodDecision treeOSA-based method10023.4618.917.540042.133.131.280068.354.953.2100093.880.179.8


## 6.3. Other methods Comparison

The suggested method is compared based on Improved Owl search algorithm algorithms and artificial neural networks by using well-known Weka software with other conventional methods in machine learning. The Weka software could be considered an assortment of the most up-to-date machine learning algorithms and tools for processing data.All features of the software are available to users in the form of proper user interfaces. As a result, they can implement diverse methods on their data and select the best algorithm for this purpose. In this part, numerous tests have been performed using various learning machines, including Weka software on the primary database, the bank’s credit risk database. The proposed learning methods are AdaboostM1, Bagging, Random Forest, Rotation Forest, Multi-Layer Perception (MLP), and AD Tree.As illustrated in Table3, the suggested method accuracy is preferable to other methods on this particular database. This is noticeable by analyzing the accuracy achieved in each process and the suggested approach. These methods have been modified under the identical conditions of 10-fold cross-validation.Table 3
Accuracy of the suggested algorithm with other methods comparison.
AlgorithmAccuracy (%)Error (%)Proposed method964.0Bagging69.730.3AdoboostM169.730.3Rotation forest73.826.2Random forest75.924.1ADTree8020.0MLP72.327.7In this research, the error of provided method was less comparing other forms; as a result, it has higher accuracy. Table4 compares the performance of this algorithm and other methods.Table 4
Implementation time comparison of the suggested algorithm with other methods.
AlgorithmRun time (seconds)Proposed method80.1Bagging266.1AdoboostM164.9Rotation forest2181.9Random forest561.2ADTree93.9MLP4033.6According to the above Table, this algorithm takes less time to run than most methods and is more than the Adaboost M1 algorithm. Due to the accuracy of the measurement and the offline nature of the forecast, this issue can be ignored. Table5 illustrates a comprehensive statistical comparison between the suggested method and the methods mentioned earlier, indicating the provided method’s high accuracy.Table 5
Comparison statistical of the proposed algorithm with other methods.
AlgorithmTPEPFNTNPrecisionRecallProposed method618821381650.8850.83Bagging606951691340.8640.79AdoboostM161685218820.8780.74Rotation forest614881631370.8750.79Random forest639631681320.9120.79ADTree5961061721280.840.78MLP5621381471550.8020.79


## 7. Conclusions

This study aims to present a combination of a new Improved Owl search algorithm, a C5 decision tree, and a neural network to forecast the bank’s credit risk. The credit risk is definable based on the probability of default of the borrower or other party of the bank in meeting its obligations under the terms of the agreement. Banks need to manage the total portfolio’s credit risk, including individual credit risk or transaction risk.Banks must observe the correlation between credit risk and other risks. Effective credit risk management could be considered a vital part of a comprehensive approach to risk management, an essential condition for the long-term accomplishment of any bank. Financial institutions have faced difficulties over the years for many reasons. However, a significant cause of severe and significant problems of the bank is still directly associated with factors. Including poor credit standards for both borrowers and counterparties, poor management of risk portfolios, paying less attention to economic changes, or other conditions, could provide a base for the credit position deterioration of the other parties to the bank.Loans are the most significant and most apparent source of credit risk for many banks; however, other credit risk sources exist throughout a bank’s activities, which are included in the bank office, commercial offices, and mentioned in the items above below the balance sheet line. Banks, in addition to loans, are increasingly concerned in the field of different financial instruments, including acceptance, interbank transactions, trade financing, foreign exchange transactions, prospective financial contracts, swaps, bonds, common stock, and optional transactions, acceptance of obligations, and issuing guarantees, and settlement of transactions with credit risk (or counterparty risk). It is essential for banks to recognize, measure, monitor, and control credit risk to provide enough capital to compensate for the facing risks.This study aims to provide a novel and convenient way by combining artificial neural networks, a new Improved Owl search algorithm, a decision tree C5 for predicting and measuring banks’ credit risk, and a solution to resolve the problems in this direction. A decision tree can be utilized for diverse types of data. This method does not require complex calculations to classify data. This method shows changes that have a particular effect on the classification. The classification is offered in the form of a series of understandable rules. In this research, implementing the suggested algorithm was analyzed to forecast the credit risk of banks. According to the implementation result, using this algorithm provides satisfactory predictions on different data. In the algorithm implementation, both the Improved Owl search algorithm and decision Tree C5 are responsible for calculating (finding) accurate and proper values for the neural network’s weights. The suggested neural network itself has a role in the fitness function of the Improved Owl search algorithm. There is a series of bank customers about whom a lot of information has been recorded and are decided to be good or bad customers. The situation of other customers will also be predicted due to the three tools of the Improved Owl search algorithm decision tree and neural network for avoiding waste of bank resources.In the future work, we will work on using deep learning-based methodology with different arrangements to provide better prediction results.



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*Source: 8458501-2021-12-28.xml*