CC BY
7ECONOMICS
ARMEN GHAZARYAN
Head of the Chair of Economic Computer Science and Information Systems of Armenian State University of Economics, PhD, Associate Professor © https://orcid.org/0000-000U6083-5489
LIANA GRIGORYAN
Head of the Department of Management Accounting and Audit of ASUE, Ph.D., professor, € https://orcid.org/0000-0002-9991-8377
GARNIK ARAKELYAN
Lecturer of the Chair of Economic Computer Science and Information Systems of Armenian State University of Economics, €> https://orcid.org/0000-0001-6217-9681
IMPLEMENTATION OF MACHINE LEARNING IN THE CREDIT RISK MANAGEMENT SYSTEM OF INDIVIDUALS
There are many problems in each credit institution. The most important of them is the risk of possible losses in lending. Within the framework of the topic, the studies conducted by other researchers were investigated, from which it was concluded that machine learning tools are often used to optimally solve the above-mentioned problem. Real data on credits were used as a basis for modeling in the work. In this work, based on the available data, several machine learning models were developed, from which the best one was selected, which can contribute to the improvement of the credit risk management process. During the work, the logical connections between data and their interaction with each other were revealed. Then, based on the work done, the appropriate models were built, the quality of which was checked using various tools. The obtained models were compared and the best one was selected. The obtained results are practically applicable and show that each bank and credit organization can develop a better solution based on the large databases they have, which will contribute to curbing credit risk and reducing costs.
Keywords: Credit risk, logistic regression, decision trees, random forests JEL: C81, C88
DOI: 10.52174/1829-0280_2022.5-123
Introduction. The economic situation of recent years has shown that some of the organizations included in the financial and banking system of the RA are quite vulnerable from the point of view of credit risk management, due to which these structures suffer significant losses. In addition, it is necessary to note that credit operations are one of the prerequisites for ensuring the normal functioning of the economy. History shows that credit risk management occupies a unique place in the financial and banking system. To assess and control risk when lending to individuals, credit institutions use various developed models. In this case, the FICO Scoring statistical model is being widely used.
Depending on the borrower's score, credit organizations make a decision on granting the loan. However, this decision often is made incorrectly, due to which the quality of the credit portfolio deteriorates and significant losses occur. That is why it is necessary to introduce a reasonable decision-making system, and by this, the topicality of the research is conditioned.
Currently, banks in several developed countries are solving the above-mentioned problem using machine learning tools.
The purpose of the work is to research the customers' behavior based on certain examples, to develop several decision-making models, and to choose the best of these models.
The implementation of the research objective can be applied in credit organizations, which will contribute to the optimal management of credit risk and cost reduction in these organizations.
Literature review. Within the framework of this research, various articles, theses, books, etc. Related to the topic were studied. Let us discuss some of them.
In the article "Development of the Scoring Map Using Logistic Regression" written by Sorokin A. S. econometric modeling of credit default probability based on logistic regression is considered. In this work, particular attention is paid to the methodology of building the model. The transformation of the obtained coefficients of the logistic regression model into scoring maps is also presented. An example of creating a scoring map is presented.1
The article "Implementation of the Credit Scoring System in the Bank" written by Stroev A. A. is devoted to practical issues of scoring algorithm construction. This article considers the process of data preparation for modeling and calculations. The paper also provides a comparative analysis between three modeling algorithms; logistic regression, decision trees, and neuronal networks.2
1 A. S. Sorokin, "Construction of scoring maps using a logistic regression model", Internet journal "SCIENCE", No. 2 (21) 2014 Date of last access: 17.10.22p. 22:58:00 (In Russian)
2 A.A. Stroev, SAS company, "Implementation of a credit scoring system in a bank", Methodical journal Calculations and operational work in a commercial bank, (In Russian) number 6/2004, Moscow Date of last access: 17.10.22 p. 22:58:00
In the article "Credit Scoring with Boosted Decision Trees" written by Bastos J. a credit rating model based on augmented decision trees, which is a robust machine learning method, is considered. It is a classifier built from several decision trees. Each tree makes a prediction, after which the answer with the most votes is accepted as the answer of the classifier. Obtained simulation results show that augmented decision trees consider being a competitive method for building a credit scoring model.3
According to the study by G. Paleolog, based on a sample of Italian IBM customers, the main goal is to create and test reliable models that can develop new predictions even in the case of missing information in the scoring system. The essence of the method is to replace the missing data with the so-called "shadow data" and to implement group classification methods. This method is suitable for highly unbalanced data such as credit data. The shadowing stages are structured with an individual cross-check cycle that creates dependencies between different credit inquiries. The methodology was implemented using several classifier methods, support vector machines (Support Vector Machines-SVM), nearest neighbors (K-nearest neighbor) and decision trees.4 From the study of the above-mentioned articles, as well as other theses and books, it can be concluded that the logistic regression model is the method most often used in the banking system as a method for developing a model for assessing the creditworthiness of customers.
Research methodology Data processing methods were used during the research. To identify patterns in the data, the method of correlation analysis was used.
Correlation is the relationship between two variables. This coefficient shows the linear relationship between the variables. Given the fact that the quality of the machine learning model can be improved by pre-processing the data, data clustering mechanisms have been applied (Data binning).5 Data were then standardized for modeling.
The main purpose of standardization is to bring all variables into one common form. In practice, there are various methods of standardization. The Weights of Evidence (WOE) method is used in this paper because it is widely used and understood.6 For each group of grouped factors, WOE is calculated using the following formula.
_ _ Pprrfhtcge of aood m Cfcfficss Dirff KtC£P of ocd li! tfiiflCSi
where In is the natural logarithm.
Logistic regression, decision tree, and random forest models were used to build the machine learning models in the research.
3 J. Bastos, «Credit scoring with boosted decision trees», CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon, 2008.
4 Paleologo, G., Elisseeff, A., & Antonini, G., 2010. Subagging for credit scoring models. European Journal of Operational Research, 201, 490-499. Date of last access: 16.10.22 p. 22:25:00
5 https://www.geeksforgeeks.org/binning-in-data-mining/. Date of last access: 17.10.22 p. 22:58:00
6 https://medium.com/mlearning-ai/weight-of-evidence-woe-and-information-value-iv-how-to-use-it-in-eda-and-model-building-3b3b98efe0e8. Date of last access: 16.10.22 p. 23:15:00
Logistic regression (Logit model) is a statistical model used to predict the probability of an event occurring by comparing it to a logistic curve. This model returns a binary event probability (between 0 and 1) as an answer.7
A decision tree is a model that uses a tree-like data structure that represents several possible ways to solve a problem and the final result for each of them.8 Graphically, it can be presented in the form of a tree structure, where decision-making moments correspond to so-called decision nodes, in which branching of the process takes place, dividing it into so-called branches depending on the choice made. The final nodes are called leaves (leaf nodes), each of which represents the final result of the decision made9.
A random forest model is a set of pre-selected number of decision trees, where all trees have the same parameters. Each tree in the forest is allowed to view not all the data, but only a sub-sample, which is formed by both rows and columns (randomly selected). Based on factors and samples, cross-validation training is performed for each tree. In these models, the quality of each individual tree is generally poor, but the quality of the overall forest model is significantly higher through a group of these trees.
To avoid the problem of overtraining in machine learning models, the cross-validation training method was used. To check the qualities of the obtained models, different types of methods were used and various indicators were calculated, for example, recall score, precision score, F1 score ROC curve, etc.10. From the received models, the best model was selected through comparative analysis.
Analysis. The work is based on real lending data obtained by Unibank OJSC, which operates on the territory of the Republic of Armenia. There are missing values in the data for only one variable (Number of delays) that has been processed. Some variables were originally coded. Based on the information received from the bank, such variables were translated into understandable language for further analysis. Such data cleaning was carried out using the Excel program. There are 13 variables in the given dataset:
• Marital Status. In the original data, this variable had the values M1, M2, M3, and M4. Each value has been changed to "Married", "Widow", "Divorced", and "Single", respectively;
• Education. In the original data, this variable had the values E1, E2, E3, and E4. Each value has been changed to "Academic degree", "Higher education", "Medium professional", and "High school", respectively;
• Availability of property. In the original data, this variable had the values P1, P2, P3, and P4. Each value has been changed to "Availability of real estate", "Availability of movable property", "Availability of real estate and movable property", and "Absence", respectively;
7 https://www.ibm.com/topics/logistic-regression. Date of last access: 17.10.22 p. 23:08:00
8 https://www.ibm.com/topics/decision-trees. Date of last access: 17.10.22p. 23:30:00
9 https://www.ibm.com/cloud/learn/random-forest. Date of last access: 17.10.22p. 23:22:00
10 https://www.pycodemates.com/2022/05/precision-and-recall-in-classification.html. Date of last access: 17.10.22 p. 20:22:00
• Sex. In the original data, this variable had the values S1 and S2. Each value has been changed to "Female", and "Male", respectively;
• Borrower's Age. The variable is numeric and contains values from 20 to 65;
• The number of days past due in the last 12 months. The variable is numeric and contains values from 0 to 1600;
• The number of delays. The variable is numeric and contains values from 1 to 27. This variable contains missing data. Based on the information received from the bank, it became clear that these clients do not have overdue debts. Based on this, the missing values were changed to 0;
• The number of changes in risk classes. The variable is numeric and contains values from 0 to 28;
• Credit load. The variable is numeric and contains values from 0 to 2,984,303. Data are presented in AMD;
• Credit history length. The variable is numeric and contains values from 0 to 488. Data is presented in days;
• Maximum repaid loans. The variable is numeric and contains values from 0 to 25,131,048. Data are presented in AMD;
• Contract sum. The variable is numeric and contains values from 30,000 to 2,300,000. Data are presented in AMD. There are some outliers in the data. These are the clients to whom the bank has issued a loan in the amount of more than AMD 1,000,000. Similar observations 7. It was calculated that the removal of observational data does not significantly affect the quality of the models. Therefore, when creating models, these observations were not removed from the dataset;
• Default. Binary data is presented. 1 - default, 0 - no default. Based on the information received from the bank, a default is considered the presence of overdue obligations for 90 days or more at least once in the last year.
There are 4603 observations in the dataset.
For modeling, the "Default" variable was selected as a predicted (dependent) variable.
Both the analysis and the construction of the models were carried out using the Excel program, the Python programming language, and its corresponding libraries. These are Pandas, Matplotlib, Seaborn, Scikit-learn libraries.
The data file with csv extension was then imported and a preliminary examination was performed.
Descriptive statistics of numerical data are shown below (Table 1). The table shows that there are no missing data since the number of values in all variables is 4,603. The table shows the arithmetic mean, minimum and maximum values for all indicators, as well as percentiles of 25%, 50%, and 75%.
The table below shows that the standard deviation of the variables Credit load, Credit history length, Maximum repaid loans, and Contract sum is high. This is due to the fact that the data contains customers with different credit
histories. For example, one client may have no credit history, while another client may have a large debt burden. This is due to the credit policy of the bank.
Given the characteristics of the data, the standard deviation of other parameters is within acceptable ranges.
Table 1
Descriptive statistics
3
4- "C
O tí IS
Ew_c c TO 3« _ ° < Z-C.S E
4603 37,54 12,09 20 27 36 48 65
4603 4603
4,03 38,54 0 0 0 0
1600
4603
4603 4603
4603
4603
2,71 4,28 0 0 1
4
67
0,55 471551,5 1,46 537158,022
59,39 332959,8464 254229,2 61,27 920340,3925 144770,9
0 0 0 0 28
0 0
300000 825099,5 2984303
0 13 40 86 488
0 0
150000 376000
30000 181900 200000 377050
25131048 2300000
4603 0,426027 0,494551 0 0 0 1 1
A correlation matrix was constructed (Table 2), from which it can be seen that there is a stronger relationship between credit burden and length of credit history, default and credit burden, number of delinquencies, and number of changes in risk class.
Table 2
Correlation analysis
Age Number of days past due in the last 12 months Number of delays Number of changes in risk classes Credit load Credit history length Maximum repaid loans Contract sum Default
Age 1
Number of days past due in the last 12 months 0,01 1
Number of delays 0,05 0,34 1
Number of changes in risk classes 0,08 0,38 0,57 1
Credit load 0,14 -0,01 0,05 0 1
§§■ 02, 0,09 0,35 0,22 0,5 1
Maximum repaid loans 0,12 0,14 0,18 0,12 0,17 0,26 1
Contract sum 0,07 0,04 0,01 0,01 0,07 0,1 0,13 1
Default -0,08 0,08 0,04 -0,05 0,15 -0,05 0 0,1 1
In the framework of the work, an analysis of various variables was carried out, from which it can be seen that defaulted clients are quantitatively concentrated in the age group from 20 to 25 (50.49%, Table 3, Figure 1), in the group of clients with secondary professional education (42.83%, Table 4, Figure 2), as well as in the group of clients whose husband died (54.14%, Table 5, Figure 3). From the analysis, it can be seen that the most overdue clients are male representatives (46.83%, Table 6, Figure 4).
Analysis of age groups
Age range
1. 20-25
2. 26-35
3. 36-50
4. 51 +
545
348
893
Table 3
| Default Total Default %
No Yes
457 466 923 760 589 1349 880 558 1438 50,49% 43,66% 38,80%
38,97%
1,600 1,400 1,200 1,000 800 600 400 200
1,349
923
1,438
893
1. 20-25
2. 26-35
3. 36-50
4. 51+
Figure 1. Analysis of age groups
Table 4
Analysis of customers depending on the level of education
Education
Academic degree
High school
Higher education
Medium professional
| Default Total Default %
No Yes
343 255 598 3 2 5 626 453 1079 42,64% 40,00% 41,98%
1670
1251
2921
42,83%
3,500 3,000 2,500 2,000 1,500 1,000 500
2,921
598
1,079
I
Academic degree High school Higher education Medium professional
Figure 2. Analysis of customers depending on the level of education
Table 5
Analysis of clients by marital status
Marital Status
Divorced
Married
e
Widow
| Default Total Default %
No Yes
46 13 59 2139 1428 3567 42 3 0 72 415 490 905 22,03% 40,03% 41,67% 54,14%
4,000 3,500 3,000 2,500 2,000 1,500 1,000 500
3,567
59 Divorced
Married
72 Single
905
I
Widow
Figure 3. Analysis of clients by marital status
5
Table 6
Analysis depending on the customer's gender
Female
Male
| Default Total Default %
No Yes
1 492 1 150 948 1 013 2 440 2 163 38,85% 46,83%
1,600 1,400 1,200 1,000
1,492
948
1,013
800 Female __I Male
600 400 200
No default Default
Figure 4. Analysis depending on the customer's gender
After completing the analysis, it is necessary to develop the machine learning models (logistic regression, decision tree, and random forest), evaluate their quality indicators, and perform a comparative analysis to choose the best of these models.
For modeling purposes, it is necessary to group the available data and perform standardization for all groups using the above-mentioned WOE method.
Table 7 shows the grouping of data in terms of available observations and the values obtained as a result of the standardization of each group.
Table 7
Data regrouping, WOE calculation
Group Sum Bad Good The Share of the Bad WOE
Female Sex 612,677,502 252,268,372 360,409,130 41.2% 0.168654027
Male 557,539,675 277,974,655 279,565,020 49.9% -0.182387387
Education
Academic degree 155,561,450 67,882,200 87,679,250 43.6% 0.067819077
Higher education 291,127,220 128,092,010 163,035,210 44.0% 0.053125014
Medium professional 722,189,907 333,875,217 388,314,690 46.2% -0.037043596
High school 1,338,600 393,600 945,000 A u:i:+.. »—_____+.. 29.4% 0.68775742
nYaiiaumijr ui w^ci iy Availability of real estate 70,759,900 31,236,900 39,523,000 44.1% 0.047190351
Availability of movable property 280,834,370 123,675,310 157,159,060 44.0% 0.051506408
Availability of real estate and movable property 59,682,480 24,920,490 34,761,990 41.8% 0.14474185
Absence Married 758,940,427 913,540,317 350,410,327 Marital statu 392,974,777 408,530,100 s 520,565,540 46.2% -0.034631577 43.0% 0.093078026
Widow Divorced 225,660,060 13,085,400 125,451,550 3,069,200 100,208,510 10,016,200 55.6% -0.412758855 23.5% 0.994694499
Single 17,931,400 8,747,500 9,183,900 48.8% -0.139408336
Borrower's age
20-25 210,717,210 113,159,800 97,557,410 53.7% -0.336452296
26-35 350,813,150 166,827,850 183,985,300 47.6% -0.090198921
36-50 370,737,745 153,233,915 217,503,830 41.3% 0.162158508
51+ 237,949,072 97,021,462 140,927,610 40.8% 0.185221801
Number of days past due in the last 12 months
0-30 31 + 0 1,155,315,600 14,901,577 540,572,830 516,660,850 638,654,750 13,582,177 1,319,400 Number of delays 219,287,600 321,285,230 44.7% 0.023885006 91.1% -2.519673673 40.6% 0.193852843
1 0-1 2+ 0 629,644,347 310,955,427 318,688,920 Number of changes in risk classes 49.4% -0.163526468 46.4% -0.044445303 36.6% 0.359864171 28.6% 0.729183998
1 ,uoo, iao,oou 132,020,817 317,685,120 +oi,o/o,/uu ooo,oi/,oou 48,364,327 83,656,490 Credit load 90,703,290 226,981,830
1-300,000 300,001 + 0-270 207,519,120 94,002,700 113,516,420 645,012,937 345,537,037 299,475,900 Credit history length 1,156,562,627 524,693,077 631,869,550 45.3% 0.000531649 53.6% -0.331158231 45.4% -0.002222854
271-365 366+ 10,142,950 4,176,15 0 5,966,8 00 3,511,600 1,373,800 2,137,800 Maximum repaid loans 41.2% 0.168728659 39.1% 0.254104298
0-350,000 810,314,677 365,608,277 444,706,400 45.1% 0.007759468
350,001 + Up to 200,000 200,001-400,000 359,902,500 520,208,537 418,168,380 164,634,750 195,267,750 Contract sum 212,833,517 307,375,020 176,410,450 241,757,930 45.7% -0.017450032 40.9% 0.179465974 42.2% 0.127031212
400,001 + 231,840,260 140,999,060 90,841,200 60.8% -0.627732638
In this work for machine learning, the calculated WOE values for each group are taken as independent variables, and as a dependent variable that needs to be predicted, the attribute "Default", which can be 0 (not default) or 1 (default).
The above WOE variable values and the predicted variable were compiled into a csv file using the Excel program. Based on these data, it is necessary to create appropriate models. Then, to get the best result, the appropriate parameters should be searched through cross-validation. For training, the data were divided into two groups; training (x_train, y_train) and testing (x_test, y_test) with a ratio of 65% and 35%. Taking into account that the logistic
regression model is often used in scoring maps, it was decided not to choose the best parameters for that model.
After performing the above-mentioned actions, the models were trained with x_train and y_train data. The training was carried out based on the identified best parameters for the models (except for the logistic model). This is done by the GridSearchCV function of the Sklearn library. So, based on the available data for decision trees, the best parameters were to calculate using the Gini coefficient and a tree depth of 9. For random forests, the best parameters found are a depth of 10 for each tree and a number of 150 trees.
Models created with those parameters are saved, and regarding those models, a quality index was calculated using the sklearn library's scor method. This is the simplest metric for evaluating the quality of a model and shows the percentage of correctly predicted outcomes. So for logistic regression, this indicator calculated on the training base was 62.0%, and for the test base, it was 58.9%. For decision tree models, this indicator calculated on the training base was 71.4%, and for the test base it was 64.9%, and for random forest models 74.4% and 64.1%, respectively.
Based on the test data, the Precession and Recall coefficients were also calculated, indicating the quality of the models. Then, F1 score was calculated for each model, which ideologically combines the information of the above-mentioned indicators (Table 8).11 Below are the formulas for calculating these indicators (1), (2), (3).
TP
PrSCBSion =—-rT". (11
TP + FP TP
Recall = —-—. (2)
TP + FN ■ '
2 * Precision * Recall
Precision + Recall ^ ^
where TP (True positive) is the number of correctly predicted observations; FP (False positive) is the number of incorrectly positively predicted observations; FN (False negative) is the number of mis-negatively predicted observations.
Table 8
Qualitative indicators of models based on test data
Models | Test data |
I Precession 1 Recall I F1 Score I AUC I
Logistic regression Decision tree Random forest 0.5549 0.2675 0.3610 0.6275
0.6338 0.6364 0.4506 0.4006 0.5268 0.4917 0.6661 0.6801
After all this, the ROC curve for each model was constructed. It is a graph that allows you to evaluate the quality of the classifier (model), showing the ratio between correctly classified and incorrectly classified objects (figure 5). Quantitative interpretation of the ROC provides the AUC (Area under Curve) indicator, the surface bounded by the ROC curve, and the axis of the proportion
11 N. Shakla, "Machine Learning & TensorFlow", - Peter, 2019, p. 118 Date of last access: 10.10.22 p. 21:25:00 (in Russian)
of false positive classifications. The higher the AUC index, the better the classifier is, while a value of 0.5 indicates the inappropriateness of the chosen classification method. A value less than 0.5 indicate that the classifier is doing just the opposite.12 Below is the formula for calculating AUC (4).
Table 9 below quantitatively presents the prediction results of all models based on test data, and in Table 10, based on the test data, the results of the predictions of defaulted customers only.
Table 11 presents the results of forecasts of defaulted customers based on test data in quantitative terms, from which it can be seen that in the case of implementing decision trees, the considered financial organization can reduce its expenses by 94.9 million AMD.
Based on the work done, it can be noted that the best of the developed models is the decision tree, because according to Table 8, this model can work more effectively to identify defaulted customers.
Figure 5. ROC curves of the models
Table 9
Prediction results based on test data (quantitative data are presented)
1 Prediction status Decision tree Random forest 1
Right 950 1046 1033
Wrong 662 566 579
Sum 1612 1612 1612
12 https://www.ibm.com/docs/ru/spss-statistics/beta?topic=features-roc-analysis. Date of last access: 17.10.22 p. 21:25:00
Table 10
Prediction results only for defaulted customers based on test data (quantitative data are presented)
Prediction status Logistic regression Decision tree Random forest
Right 187 315 280
Wrong 512 384 419
Sum 699 699 699
Table 11
Prediction results only for defaulted customers based on test data (dimensional data are presented)
Model type Predicted wrong Predicted right Sum The proportion of right predictions
Logistic regression [lwicinn frpp 122,573,150 90 006 150 62,366,862 94 933 862 184,940,012 184 940 012 33.7% 51.3% 44.7%
UCLIolUII ll CC Random forest 102,357,550 82,582,462 184,940,012
Conclusions. It is important for credit organizations to have a clear understanding of the need to identify, measure, monitor, and manage credit risk. To curb this risk it is necessary to introduce a reasonable decision-making system, and by this, the modernity of the research is conditioned.
The purpose of the work was to research customers' behavior based on certain examples, to develop several decision-making models, and to choose the best of these models. One of the options for the optimal solution to this problem is the use of machine learning methods because currently, credit organizations have generated a fairly large amount of data that needs to be processed, researched, and then build a decision-making model based on the obtained results.
Taking into account that the problem of making a decision on providing a loan during lending is a classification problem, a logistic regression model, a decision tree model, and a random forest model of machine learning were used in this work.
For the work in this research, it was necessary to solve the following problems;
1. Examine existing data to identify relationships between indicators;
2. Develop decision-making models, and test them.
As a result of the analysis of the available data, three models were developed, of which the decision tree model was considered the best according to the obtained results.
Given the peculiarities of risk management and regulatory legal acts, when issuing loans, banks are required to form reserves for possible losses on loans. As the customer's loan service worsens, banks are forced to increase the amount of these reserves, which leads to a decrease in their profits.
Loans from defaulted customers in most cases cannot be recovered, which causes financial institutions to suffer losses in the amount of the entire loan.
As part of this study, using test data that was not used to create machine learning models, we estimated the losses that a bank can reduce when using a decision tree model. This is the sum of loans of defaulted borrowers (from a test
dataset that the model has not yet seen) that the bank has issued a loan without using machine learning models and that the decision tree has classified as bad borrowers. Table 11 shows that the amount of these loans is 94.9 million AMD. It can also be seen from this table that the amount of loans issued, for which the decision tree during predictions made a mistake, is the smallest compared to other models (90.0 million AMD).
It is necessary to note that the quality of this or that machine model also depends on the qualitative and quantitative characteristics of the data used for modeling. Therefore, in the presence of other data, another type of model can be recognized as the best.
From the results of the work, it can be concluded that the application of machine learning models in the financial and banking system can reduce the impact of existing credit risks and the costs of organizations.
In addition, the use of machine learning implies the complete automation of the decision-making process, which will lead to a reduction in the number of errors made during manual calculations. As a result of this, the time for issuing a loan will also be reduced, which will lead to the optimization of processes and an improvement in the quality of loan servicing.
References
1. N. Shakla, "Machine Learning & TensorFlow", - Peter, 2019, p. 336.
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3. A.A. Stroev, SAS company, «Implementation of a credit scoring system in a bank», Methodical journal Calculations and operational work in a commercial bank, number 6/2004, Moscow
4. J. Bastos, «Credit scoring with boosted decision trees», CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon, 2008.
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DOI: 10.52174/1829-0280_2022_5_123
АРМЕН КАЗАРЯН
Заведующий кафедрой экономической информатики и информационных систем АГЭУ, кандидат экономических наук, доцент
ЛИАНА ГРИГОРЯН
Заведующая кафедрой управленческого учета и аудита АГЭУ, кандидат экономических наук, профессор
ГАРНИК АРАКЕЛЯН
Преподаватель кафедры экономической информатики и информационных систем АГЭУ
Применение машинного обучения в системе управления кредитными рисками физических лиц,- В каждом кредитном учреждении есть множество проблем. Важнейшим из них является риск возможных потерь при кредитовании. В рамках исследования были изучены труды ряда исследователей, на основе которых был сделан вывод о том, что для оптимального решения вышеуказанной задачи часто используются средства машинного обучения. В качестве основы для моделирования в работе использовались реальные данные по кредитам. В данной работе на основе имеющихся данных было разработано несколько моделей машинного обучения, из которых была выбрана лучшая, способная помочь совершенствованию процесса управления кредитным риском. В ходе работы были выявлены логические связи между данными и их взаимодействие друг с другом. Затем на основе проделанной работы были разработаны соответствующие модели, качество которых проверялось с помощью различных инструментов. Проведено сравнение полученных моделей и выбрана лучшая. Полученные результаты применимы на практике и показывают, что каждый банк и кредитная организация может разработать лучшее решение на основе имеющихся у них больших баз данных, что будет способствовать сдерживанию кредитного риска и снижению затрат.
Ключевые слова: кредитный риск, логистическая регрессия, деревья решений, случайные леса С81, С88
РО!: 10.52174/1829-0280_2022_5_123
