Banking and Banking Management
Yaqoob shahniaee; Amin Nazemi; Navid Reza Namazi
Abstract
The main goal of this study is to design a suitable model of asset and Liability management of the Agricultural Bank with the purpose of achieving the set goals of the bank.In this research, an attempt has been made to present the optimal value of assets, debts and cash in accordance with the structure ...
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The main goal of this study is to design a suitable model of asset and Liability management of the Agricultural Bank with the purpose of achieving the set goals of the bank.In this research, an attempt has been made to present the optimal value of assets, debts and cash in accordance with the structure of the financial statement. Given the determination of multiple goals and limitations in the banking system and the experience of the past years, the model used in this paper is the fuzzy ideal planning model with fuzzy constraints. The proposed model of the paper has the ability to present the optimal values of each items of balance sheet for the upcoming years according to the conditions of the previous years. In order to reach the final solution, the number of ten ideals has been determined, and by solving the general model, the value of the objective function has been significantly improved, and by converting fuzzy numbers into definite numbers and by using the fuzzy ideal planning model, a suitable model of asset and debt management of financial year 2023 has been determined for this bank. And based on the results arising from the implementation of the model, we achieved seven ideals, including maximizing profit in the amount of 32,433,646 million Rials (31,634,068), complying the limitation of the ratio of facilities to deposits in the amount of (0.86) 0.85, improving the bank's share from the deposits of the bank system in the amount of 3,553,820,000 million Rials (3,273,413,445), complying the limitation of capital adequacy in the amount of (0.08) 0.0825, reducing the volume of investment in tangible fixed assets in the amount of (0.77) 0.66, green banking in the amount of (0.9) 0.091 and liquidity risk in the amount of 70,108,198 million Rials (0) and not achieved three less important ideals include increasing the amount of the items of balance sheet 4,433,821 302 million Rials (4,514,025,887), increasing the value of some items of assets compared to the total by (0.9) 0.84 and the amount of claims from banks and credit institutes is greater than the amount of their debits by the amount of 0.234 (1).
Banking and Banking Management
Aliyeh Kazemi; Amir Moghadamfalahi; Ali Abdali; Sara Aryaee
Abstract
Nowadays, money laundering has become a serious threat to the world economy. Traditional methods of Anti Money Laundering (AML) are costly and inefficient. Recently, data mining techniques have been developed and have been considered as appropriate methods to detect money laundering activities. The purpose ...
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Nowadays, money laundering has become a serious threat to the world economy. Traditional methods of Anti Money Laundering (AML) are costly and inefficient. Recently, data mining techniques have been developed and have been considered as appropriate methods to detect money laundering activities. The purpose of this research is to detect money laundering suspicious cases which might need more detailed scrutiny using data mining algorithms with real banking transaction datasets. CRISP-DM would be used as the research methodology, the statistical population would be the banking transactions and samples would be the transactions of one of the bank branches. For this purpose, two main approaches are used. In the first approach, using the k-means algorithm, financial transactions of banking accounts are clustered. Then, using anomaly detection techniques, abnormal transactions that might be suspicious of money laundering and need to be scrutinized in more detail have been detected. In the second approach, a novel technique using Benford’s law and GANs algorithm has been introduced. It can detect financial accounts that used concocted amounts in their transactions and might be suspicious of financial fraud and money laundering. The first approach can identify accounts with outliers in their transactions with an accuracy of about 93%, and the second approach can identify suspicious accounts that do not use professional methods to hide fake figures in their transactions with an accuracy of about 60%. to recognize correctly.
