Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran
ObjectiveIn banking, customer management and validation are crucial to maintain financial security and organizational stability. A fundamental challenge in this area is the identification of appropriate indicators for validating and ranking customers. Because these customers generally have limited a...
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University of Tehran
2024-06-01
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| Series: | تحقیقات مالی |
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| Online Access: | https://jfr.ut.ac.ir/article_98033_59097b44826a2e59ed3c94ef18340c28.pdf |
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| author | Azadeh Ahmadi Kousha Faegh Ahmadi Mohammad Hossein Ranjbar Hamidreza Kordlouie |
| author_facet | Azadeh Ahmadi Kousha Faegh Ahmadi Mohammad Hossein Ranjbar Hamidreza Kordlouie |
| author_sort | Azadeh Ahmadi Kousha |
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| description | ObjectiveIn banking, customer management and validation are crucial to maintain financial security and organizational stability. A fundamental challenge in this area is the identification of appropriate indicators for validating and ranking customers. Because these customers generally have limited access to financial and credit information and cannot provide guarantors or good credit records, it is very challenging to determine correct and reliable indicators. Also, to score customers, there is a need to determine an appropriate and fair scoring system. This system should be able to place customers in different categories by considering credit and behavioral criteria and assigning them appropriate points based on their performance. In addition, there is a need to develop methods for evaluating and monitoring customers over time. This research aims to identify the indicators of validation and ranking of customers in micro-lending in Iran’s Middle East Bank. MethodsThis research is applied-contextual in terms of purpose and exploratory in terms of method. The statistical population of this research includes all retail banking clients of Digital Middle East Bank who seek low-interest loans at an annual rate of 2%, with a penalty rate of 6%. Statistical methods in this study were carried out in two phases: descriptive and inferential statistics. In the descriptive statistics section, various personality factors including age, gender, education, occupation, current debt status within the banking system, bounced checks history, money laundering records, bank account balance, transaction history, geographic location (residence and workplace), mobile phone model and operating system, as well as credit rating obtained from Iran's credit rating consulting company, were analyzed and presented using tables and graphs. Naive Bayes, Meta, Attribute Selected Classifier, and j48 algorithms were implemented and WEKA software was used to classify criteria and create patterns. Also, to evaluate the validation model and ranking of customers of unsupported microlending, the T-test was used at the significance level of 0.25. ResultsThe findings indicate that when assessing a loan application, the following indicators should be considered to determine the applicant's ability to repay the loan or provide suitable collateral. Applicants demonstrating most or all of the following indicators are more likely to meet loan repayment requirements or offer adequate collateral: First, the person's previous loans have been settled 30 days and 60 days after the loan maturity date in the granting bank. Second, the person's previous loans have been settled in other banks. Third, the higher the requested loan amount, the better. Fourth, the person's age is above middle age. Fifth, the person's degree is not a bachelor's degree, diploma, or sub-diploma. Sixth, the person's score is above 40. Seventh, the operating system of the person's phone is not Android. Eighth, the person's phone model is not SAMSUNG or XIAOMI. Ninth, if the investigations related to the person's money laundering status are negative, then preferably, and if necessary, the loan of that person can be approved. ConclusionIn this approach (unsupported micro-lending), banks should extend loans to individuals only after acquiring comprehensive knowledge about them before initiating any new credit arrangement. It is vital to gather the required data and ensure their creditworthiness and positive reputation. Banks need to obtain reliable and detailed information about borrowers, as extending facilities for maximum profitability also exposes the bank to associated risks. |
| format | Article |
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| institution | Kabale University |
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| publishDate | 2024-06-01 |
| publisher | University of Tehran |
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| spelling | doaj-art-13d990ad337749658fbada55d28ee1ab2025-02-11T14:02:49ZfasUniversity of Tehranتحقیقات مالی1024-81532423-53772024-06-0126241543810.22059/frj.2024.370376.100755198033Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in IranAzadeh Ahmadi Kousha0Faegh Ahmadi1Mohammad Hossein Ranjbar2Hamidreza Kordlouie3Ph.D. Candidate, Department of Economic Sciences, Qeshm Branch, Islamic Azad University, Qeshm, Iran.Assistant Prof., Department of Financial Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran.Associate Prof., Department of Accounting and Financial Management, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran.Associate Prof., Department of Business Management, Islamshahr Branch, Islamic Azad University, Tehran, Iran.ObjectiveIn banking, customer management and validation are crucial to maintain financial security and organizational stability. A fundamental challenge in this area is the identification of appropriate indicators for validating and ranking customers. Because these customers generally have limited access to financial and credit information and cannot provide guarantors or good credit records, it is very challenging to determine correct and reliable indicators. Also, to score customers, there is a need to determine an appropriate and fair scoring system. This system should be able to place customers in different categories by considering credit and behavioral criteria and assigning them appropriate points based on their performance. In addition, there is a need to develop methods for evaluating and monitoring customers over time. This research aims to identify the indicators of validation and ranking of customers in micro-lending in Iran’s Middle East Bank. MethodsThis research is applied-contextual in terms of purpose and exploratory in terms of method. The statistical population of this research includes all retail banking clients of Digital Middle East Bank who seek low-interest loans at an annual rate of 2%, with a penalty rate of 6%. Statistical methods in this study were carried out in two phases: descriptive and inferential statistics. In the descriptive statistics section, various personality factors including age, gender, education, occupation, current debt status within the banking system, bounced checks history, money laundering records, bank account balance, transaction history, geographic location (residence and workplace), mobile phone model and operating system, as well as credit rating obtained from Iran's credit rating consulting company, were analyzed and presented using tables and graphs. Naive Bayes, Meta, Attribute Selected Classifier, and j48 algorithms were implemented and WEKA software was used to classify criteria and create patterns. Also, to evaluate the validation model and ranking of customers of unsupported microlending, the T-test was used at the significance level of 0.25. ResultsThe findings indicate that when assessing a loan application, the following indicators should be considered to determine the applicant's ability to repay the loan or provide suitable collateral. Applicants demonstrating most or all of the following indicators are more likely to meet loan repayment requirements or offer adequate collateral: First, the person's previous loans have been settled 30 days and 60 days after the loan maturity date in the granting bank. Second, the person's previous loans have been settled in other banks. Third, the higher the requested loan amount, the better. Fourth, the person's age is above middle age. Fifth, the person's degree is not a bachelor's degree, diploma, or sub-diploma. Sixth, the person's score is above 40. Seventh, the operating system of the person's phone is not Android. Eighth, the person's phone model is not SAMSUNG or XIAOMI. Ninth, if the investigations related to the person's money laundering status are negative, then preferably, and if necessary, the loan of that person can be approved. ConclusionIn this approach (unsupported micro-lending), banks should extend loans to individuals only after acquiring comprehensive knowledge about them before initiating any new credit arrangement. It is vital to gather the required data and ensure their creditworthiness and positive reputation. Banks need to obtain reliable and detailed information about borrowers, as extending facilities for maximum profitability also exposes the bank to associated risks.https://jfr.ut.ac.ir/article_98033_59097b44826a2e59ed3c94ef18340c28.pdfcustomer rankingmicrolendingvalidation |
| spellingShingle | Azadeh Ahmadi Kousha Faegh Ahmadi Mohammad Hossein Ranjbar Hamidreza Kordlouie Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran تحقیقات مالی customer ranking microlending validation |
| title | Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran |
| title_full | Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran |
| title_fullStr | Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran |
| title_full_unstemmed | Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran |
| title_short | Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran |
| title_sort | validation indicator identification and customer ranking in microloans a study at middle east bank in iran |
| topic | customer ranking microlending validation |
| url | https://jfr.ut.ac.ir/article_98033_59097b44826a2e59ed3c94ef18340c28.pdf |
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