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Accurate and condition-based validation will increase the value of banks, and if this is not done well, banks will face the risk of bankruptcy. The purpose of this research is to evaluate the usefulness of financial reporting information in modeling the credit rating of bank customers in listed companies. The methods used to validate the model in listed companies were grounded theory, DEMATEL, and regression. In the first stage, four main axes including (1) financial criteria, (2) non-financial criteria, (3) corporate governance criteria, and (4) market criteria for credit rating of customers were clarified by asking the opinion of 10 experts, were explained for the credit rating of customers. Three hypotheses were identified in the DEMATEL phase: the first hypothesis: There is a significant positive relationship between financial factors and bank facilities received by companies admitted to the Tehran Stock Exchange. Second hypothesis: There is a significant positive relationship between the company's market information and the bank facilities received by the companies admitted to the Tehran Stock Exchange. Third hypothesis: There is a significant positive relationship between non-financial factors and bank facilities received by companies listed on the Tehran Stock Exchange. The findings of the regression analysis did not reject the above hypotheses in 64 companies during the 5 years from 1396 to 1400. Banks should pay attention to the characteristics of companies in the existing conditions when providing facilities

With the importance of lending in the banking industry, it is very important to use the indicators affecting credit to decide on lending. The purpose of the present study is to identify and prioritize the effective features in customer accreditation using the viewpoints of bank experts in Kerman and to compare them with existing indicators in models extracted from Meta-Heuristic and Artificial Intelligence methods. The aim is to find out whether there is a match between the human views that arise from knowledge and experience and the views of artificial intelligence that look at the problem as black-box modeling. Required data were collected by questionnaire method and Quantum Binary particle swarm optimization algorithm and analyzed by Delphi. The results show that the selected indices have 80% overlap between the two methods. Due to the results of research and high accuracy of artificial intelligence techniques, it is suggested that in order to give credit to customers in banks and financial and credit institutions, to consider a higher weight for these indicators.

One of the most important risks that the banks and financial institutes face, is credit risk which is related to not-paid instalments or the instalments paid with delay by borrowers. Banks use credit scoring models In order to prevent this type of risk. The goal of this research is to investigate the joint effect of economic time cycles and the industry sector on credit scoring models we are seeking to answer the key question: “when should bank change their credit scoring models based on economic time cycles and for which industry sectors?”. The dataset of the research involves all companies that were applied for a loan in one of the Iranian major banks during the years 2008-2011. The companies have been divided into four industry sectors including “Industry and Mine”, “Oil and chemical”, “Service and Infrastructure” and finally “Agriculture”. Based on the sector of the industry and year, 54 explanatory variables, both financial and non-financial, 12 distinct industry sectors and time-specific data sets are built then classification methods were used to classify customers into two groups of defaults and non-defaults. Finally, we compared the results by Wilcoxon Test. The results show that the companies that are in the groups of Industry and Mine and Agriculture, need their own special credit scoring based on industry type model and time but two other groups don’t need of course in the studies dataset duration. Finally, the study concluded by introducing the credit scoring strategies for different four-cycle of economies

Credit risk is one of the most important risks which banks and financial organizations face. It is related to unpaid and delayed installments. Banks evaluate their customers' credit in order to prevent this hazard. Development banks, which are the focus of this research, fund facilities based on working capital, so customers sometimes do fraud in declaring working capital. Considering fraud consequences and making a credit scoring model with sensitivity to fraud are the main aims of this research. The statistical population of this research includes companies who have referred to branches of an Iranian Bank. This research includes 55 financial and non-financial variables based on the credit scoring model. In the first step, fraudulent companies have been realized. Finally, in order to offer an optimized and sustainable model through merging machine learning methods and reporting performance evaluation indicators, the impacts of fraud have been considered.

Financial crises in banking systems are due to inability to manage credit risks. Credit scoring is one of the risk management techniques that analyze the borrower's risk. In this paper, using the advantages of computational intelligence as well as soft computing methods, a new hybrid approach is proposed in order to improve credit risk management. In the proposed method, for modeling in uncertainty conditions, parameters of the neural network, including weights and errors, are considered in the form of fuzzy numbers. In this method, the underlying system is firstly modeled using neural networks and then, using fuzzy inferences, the optimal decision will be determined with the highest degree of superiority. Empirical results of using the proposed method indicate the efficiency and high accuracy of this method in analyzing credit rating problems.

Currently, in the banking system, defaults in the repayment of loans have become one of the biggest problems, and banks and financial institutions have faced many problems such as the increase in the volume of outstanding receivables due to the lack of an appropriate system for allocating facilities. Considering the importance of credit risks, commercial banks used to apply judgment methods for determining those risks. However, the use of these methods was not efficient enough due to limited human abilities and, at the same time, various factors affecting credit risks in contrast to statistical methods as well as artificial intelligence methods. For this reason, this article measures the efficiency of logistic regression models and artificial neural networks in detection of bank customers’ credit status in the period of 2009-2013. The results indicated that the total accuracy rates of the Artificial Neural Network model and the Logistic Regression model were 87% and 77.2% respectively, and the error types I and II were reduced significantly in the neural network. According to the results, statistical models cannot be expected to properly evaluate the credit risk of customers with classical assumptions such as the linear relationship between variables. Therefore, application and integration of artificial intelligence techniques is strongly recommended in this regard.

a lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance of genetic algorithms with other scoring method but there is little information regard to fitness functions while these fitness functions play a vital role in overall performance of the model. To further investigation of the problem, three different fitness functions are proposed in the current paper and their performance is compared with other scoring methods including logistic regression and data envelopment analysis. The obtained results have shown that genetic algorithms quadratic function totally outperformed other methods based on accuracy, detection and sensitivity criteria.

One of the main tasks of the financial institutions is to give loan to the customers. Prediction and evaluation of the credit risks due to loan and consequently managing this risk is one of the greatest ongoing challenges for the financial institutions. The main aim of this work is to provide an optimized logistic regression model for credit scoring of real customers. Here the effects of increasing the customer’s credit classification from two (binary) to four (multinomial) distinct groups on the results of the logistic regression has been investigated. Identification of the most important parameters in prediction of the real customers’ credit scoring is the other important outcome of this work. The results of both binary and multinomial logistic regression show the relative importance of the education level and the age of the customer compared with other independent variables. The results of this work show that either increasing the number of classification types of the dependent variable, real customer’s credit, to four distinct groups has no sharp effect on the results of the optimized models or this conclusion can be due to improper distribution of the number of customers in different groups.

Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.

expert systems can help to build banks customers' credit scoring models. Here, selection of key features of the credit scoring is important. Also, it is possible to express the features values as fuzzy. The problem is how to improve features selection by genetic algorithm, in way that these features can be employed as input in fuzzy expert system. This paper presents a hybrid credit scoring model with combination of features selection based on genetic algorithm and fuzzy expert system. The research conducted at the entry, in terms of result and objective is applied and descriptive research as case study, respectively. Ghavvamin financial and credit institution credit scoring date set is used to train and test the model. After data preprocessing, features selection is carried out using genetic algorithm. The range of selected features is determined by interview with an expert and via fuzzy logic and then credit scoring fuzzy rules can be generated. WEKA tool and fuzzy inference system (FIS) in MATLAB are used for data analysis. Results demonestare classification accouracy of the proposed model is more than the other compared methods in this paper. Fuzzy rules created by this model can be used for credit scoring of bank customers.

Nowadays, the customer's credit assessment is one of most important challenges for managers in decision making. Especially in the current situation which the number scam has increased and outstanding claims of Business Company is increasing. On the other hand decision making about customers and their credit is based on expert judgment and also the customer Information is often uncertain, ambiguous and inaccessible. So Experts based on evidence, documentation, quality information and their experiences, often with linguistic variables describe their customers and judge about them. In this research, with using the advantages of fuzzy logic and expert systems, a fuzzy expert system for solving this problem has been presented which utilizing Mamdani Inference method for inference. Finally, the system was designed and implemented in Alborz Distribution Company, and it showed 91.25 percent accuracy of the system in the customer's credit rating.

This research study aims at using Data Mining and Fuzzy Logicapproaches to classify the credit scoring of banking system applicantsas to cover uncertainties and ambiguity connected with applicantclasses and also variables that affect their behavior.The methodology, according to a standard Data Mining process, is tocollect and refine the client data, then those variables which are inlinguistic forms are converted to fuzzy variables under the supervisionof banking experts and final data are modeled using Fuzzy DecisionTree, subsequently. The unfuzzy data are also modeled using the otheralgorithms.The results of the study suggest that as far as client distinctionaccuracy is concerned Fuzzy Decision Tree produces better resultscompared to Traditional Trees, Neural Networks, and statisticalprocedures such as Logistic Regression and Bayesian Network.However, it is not as accurate as Support Vector Machine and GeneticTree. On the other hand, Fuzzy Decision Tree technique has gainedbetter prediction than prediction performance of bank credit scoringexperts.

With the continuous development and changes in the credit industry, credit products play a more important role in the economy. This has led institutions to expand the role of technology in their credit management processes. Credit scoring is a method used to estimate the probability that a loan applicant or existing borrower will default or become delinquent. There are two types of methods used for scoring: Traditional statistics models like Probit and Logistic regression and Data Mining models such as Classification and Regression Trees (CART). In spite of popularity in applying Logit model in credit assessment of applicants, it is attempted to present another method which is theoretically and empirically superior to Logit model. It is also tried to study the capability and accuracy of this method in comparison with Logit model. In this paper, we have examined the performance of different models in credit scoring on real data of a bank and the two approaches above are compared as well. After building a model using Logistic regression; we have built a model using classification and regression trees. Our aim is to emphasize on the specification of CART and testing its capability and comparing its accuracy with the Logit model. The results reveal the accuracy of CART through a bootstrap simulation. Finally it is suggested that classification and regression trees method could be used in credit scoring process instead of Logit model.

Traditional methods of deciding whether to grant credit to a particular individual use human judgment of the risk of default based on experience of previous decisions. However, economic pressures resulting from increased demand for credit, allied with greater commercial competition and the emergence of new computer technology have led to development of sophisticated statistical models to aid the credit granting decision making process. Credit scoring is the name used to describe this process of determining how likely applicants are to default with their repayments. Credit scoring has some obvious benefits that have led to its increasing use in loan evaluation. For example, it is quicker, cheaper and more objective than judgmental method. A wide range of statistical methods such as discriminant analysis, logistic regression, and neural networks have been applied for credit scoring. In this paper, we design a neural network credit scoring system for classifying the applicants of personal loans in bank and compare the performance of this model with discriminant analysis and logistic regression models. The results of this investigation show that the neural network model is more accurate and more flexible than discriminant analysis and logistic regression.

Applying logistic Regression Model, this research paper is aimed tofigure out the determinants of credit risk among Agricultural Bank'sclients of Iran and establish a model to assess the credit risk amongthem. To achieve the focus of this research, the qualitative andfinancial data of ٢٠٠ randomly sampled companies which hadreceived credit facilities from different branches of Agricultural Bankin Tehran during ١٩٩٩ – ٢٠٠٤ is evaluated here. Considering creditfiles of each sample, ٣٦ explanatory variables including qualitativeand financial ones were sampled Finally, applying logistic regressionanalysis, ١٧ variables, mostly have had significant effect on credit riskand unique low risk and high risk clients were selected. Based onstatistic indices, these estimated functions are considered significantlyin high reliability according to goodness of fit indices anddistinguishing potentials of the model.Findings of this research confirm the economic and financialtheories related to determinants of credit risk and show thatdeterminants of credit risk of Agricultural Bank clients have more incommon with the ones of the other Iranian banks such as Mellat andBank of Export Development.

Decision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. Genetic algorithm can help in credit scoring of customers by choosing appropriate features and building optimum decision trees. Development process in pattern recognition and CRISP process are used in credit scoring of customers in construction of this model. The proposed classification model is based on clustering, feature selection, decision trees and genetic algorithm techniques. This model select and combine the best decision tree based on the optimality criteria and constructs the final decision tree for credit scoring of customers. Results show that the accuracy of proposed classification model is more than almost the entire decision tree models compared in this paper. Also the number of leaves and the size of decision tree i.e. its complexity is less than the other models.