تاثیر کانال های بانکداری الکترونیکی و شاخص های کارایی برسودآوری بانک مطالعه موردی بانک ملی ایران

محورهای موضوعی : فصلنامه اقتصاد محاسباتی

الیاس عسگری ^{1 *} , بیژن صفوی ²

1 - دانشگاه آزاد اسلامی واحد تهران جنوب
2 - استادیار دانشگاه آزاد، واحد تهران جنوب، دانشکده اقتصاد و حسابداری

تاریخ دریافت : 1403/09/18 تاریخ پذیرش : 1403/11/23 تاریخ انتشار : 1404/01/19

کلید واژه: بانکداری چندگانه, شاخص های مالی, شاخص های کارایی, سودآوری,

چکیده مقاله :

این پژوهش به بررسی تأثیر شاخص‌های کارایی عملیاتی و کانال‌های بانکداری الکترونیکی بر سودآوری بانک ملی ایران پرداخته است. امروزه بانکها با چالش هایی نظیر تغییر انتظارات مشتریان، تحولات فناوری، الزامات قوانین و مقررات و بحران در اقتصاد مواجه‌اند و این مسئله منجر به تغییروتحول در نظام بانکی شده است؛ بنابراین بانکها بهتر است از توسعه شرکتها یا استارتاپهای حوزه فناوری مالی به نفع خود بهره ببرند پیشرفت های اخیر در فناوری اطلاعات منجر به توسعه سریع خدمات مالی جدیدو نو آورانه شده است. در این راستا، شاخص‌های بازده دارایی‌ها (ROA) و بازده حقوق صاحبان سهام (ROE) به‌عنوان متغیرهای وابسته و متغیرهای عملیاتی شامل حاشیه عملیاتی خالص (MNO)، حاشیه سود خالص (MNI)، حاشیه سود بدون بهره (MNNI)، گردش دارایی‌ها (RA)، و ضریب اهرم مالی (MC) و متغیرهای بانکداری الکترونیکی شامل تعداد خودپردازها (ATM)، تعداد پایانه‌های فروش (POS)، تعداد حساب‌های موبایلی (NBM)، و تعداد تراکنش‌های شعب (NMC) به‌عنوان متغیرهای مستقل در مدل لحاظ شده‌اند. داده‌های تحقیق از اطلاعات مالی و عملیاتی بانک ملی ایران جمع‌آوری شده و با استفاده از مدل داده‌های پانلی تحلیل گردیده است. نتایج نشان می‌دهد که متغیرهای عملیاتی تأثیر مثبت اما محدودی بر ROA و ROE دارند، در حالی که کانال‌های بانکداری الکترونیکی تأثیر معناداری بر این شاخص‌ها ندارند. شاخص‌های کلی مدل، از جمله مقدار ضریب تعیین (R²) پایین، حاکی از آن است که مدل به‌تنهایی توانایی کافی برای توضیح تغییرات سودآوری ندارد و نیاز به افزودن متغیرهای تکمیلی و بررسی عمیق‌تر احساس می‌شود.

 

چکیده انگلیسی:

Extended Abstract

Purpose

The purpose of the research is to investigate the impact of operational efficiency indicators and electronic banking channels on the profitability of Bank Melli Iran (BMI).

Methodology

This research is applied in terms of purpose and descriptive-analytical in terms of method. In this study, the panel data method was used to examine the effect of operational efficiency indicators and electronic banking channels on the profitability of Bank Melli Iran. The data used include financial and operational information of Bank Melli Iran over a specific period of time, which was collected monthly and from reliable internal sources (such as the bank's published financial reports). Dependent variables: Including bank profitability, which is measured through two main indicators: return on assets (ROA) and return on equity (ROE (Independent variables: Including operational efficiency indicators (such as net operating margin, net profit margin without interest, asset turnover, and financial leverage ratio) and electronic banking channels (including the number of ATMs, sales terminals, mobile banking accounts, and the number of operations performed in branches). For data analysis, coding in Matlab and Python was used. In this study, a panel data model has been used, which allows for examining changes in variables over time and between units (different bank branches). Also, F and Hausman tests have been used to select the appropriate model (fixed or random effects model). The statistical population of this study includes financial and operational data related to the branches of the National Bank of Iran during a specific time period. The required data have been extracted and analyzed through financial reports and information published in banking systems.

Finding

The regression results show that the model constant is -0.40547, which means that if all independent variables are zero, ROA tends to be negative. Operating variables such as net operating margin (MNO) and net profit margin (MNI) with positive coefficients of 0.0334 and 0.0736 have a positive but weak effect on ROA, while non-interest margin (MNNI) with a positive coefficient of 0.1162 also shows a weak relationship with ROA. On the other hand, asset turnover (RA) with a negative coefficient of -0.00033595 has a small effect on reducing ROA, and financial leverage (MC) with a positive value of 0.0095377 has a positive effect. Electronic banking variables (ATM, POS, NBM, and NMC) have a very small and insignificant effect, which means that these factors do not have a significant effect on ROA. The overall model indices, including R² of 0.0224 and F-statistic of 0.5845, confirm the weakness of the model in predicting ROA changes. Overall, operational variables have a limited but positive effect on ROA, while e-banking variables do not play a significant role, and the model needs additional variables or improved analysis methods to explain ROA changes. The correlation matrix in Figure 2-1 shows the correlation between the research variables, with darker colors indicating stronger correlations. The results show that ROA is weakly correlated with other variables and that research factors have a limited effect on return on assets. Operational variables (MNO, MNI, and MNNI) have strong positive correlations, indicating their common changes and key role in the bank’s financial operations. E-banking variables such as POS and ATM have weak correlations with ROA, which is consistent with previous analyses. In general, the weak correlation of ROA with other variables indicates that the research variables alone are not sufficient to explain changes in asset returns and there is a need to examine their interaction.

Conclusion

This study aimed to investigate the impact of operational efficiency indicators and e-banking channels on bank profitability (return on assets and return on equity). The results showed that operational indicators such as net operating margin (MNO), net profit margin (MNI), and non-interest margin (MNNI) have a positive but limited impact on bank profitability. Also, financial leverage (MC) had a positive effect on return on assets and return on equity, while asset turnover (RA) showed a very weak negative effect. On the other hand, variables related to e-banking channels, including the number of ATMs (ATMs), point-of-sale terminals (POS), mobile accounts (NBMs), and number of branch transactions (NMCs), did not show a significant impact on profitability indicators. The overall indicators of the model, including the low coefficient of determination (R²) and the F-Statistic value, indicated the low power of the model in accurately predicting profitability. These results imply that operational factors play a greater role in the financial performance of the bank, while the development of electronic banking channels alone cannot significantly affect profitability. To improve the results, it is suggested that other variables, including environmental factors, the level of competition in the market, and the level of customer acceptance of electronic services, be examined in future research. Also, the use of more advanced statistical and machine learning models can be useful in more precise analysis of the relationships between variables. This research can help improve bank managers' decision-making by providing insights into operational resource management and strategic development of electronic services.

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