Predicting and optimizing the liquidity required by branch ATMs using artificial intelligence

Subject Areas : Journal of Investment Knowledge

mahdi  Afshar Ramandi ¹ (Accounting Ph.D. student, Department of accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
Farzin  Rezaei ² (Associate professor of QIAU, Department of accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
mahdi  Rezaei ³ (Assistant Professor, Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)

Received: 2021-09-04 Accepted : 2021-09-21 Published : 2022-06-22

Keywords: Risk Apptite, Bank Kshavarzi, Cash management, Optimization, Artificial Intelligence,

Abstract :

Purpose of this corpus is too optimize and predict required cash for atm devices among KESHAVARZI bank branches with in QAZVIN Province, with artificial intelligence algorithm depending on risk appetite. To achieve this purpose 2 years of daily transaction belonging to KESHAVARZI BANK branches data has been used. Which data were indicating balance, financial daily book, treasury fund and transactions and ATM transactions. The procedure of cleaning, standardization was done before the data sent to linear regression algorithms, the algorithms used in this approach are linear regression, lasso regression and ridge regression. After calculating MSE error, algorithm chose the best model with less MSE error using python programming language Also Jupiter notebook has been used as IDE This research indicates that there are a reverse meaningful relationship between cash balance and risk appetite and opportunity cost. Also a straight meaningful relationship between daily cash flow and debt-credit (which has been provided by the bank and costumers). Features and data that has been used for training an AI model revaluate again with a different values that has been also generated by AI in next step.Results shows that using artificial intelligence can predict daily cash needs with 95% accuracy with an acceptable error.

References:

• جلیلیان، نگار و زنجیرچی، سید محمود و ناصر صدرابادی، علیرضا،1399،مدیریت ریسک نقدینگی و مشارکت مشتریان در تأمین نقدینگی بانکی، دوفصلنامه کاوش های مدیریت بازرگانی، دوره: 12، شماره: 23، ص 125-146
  
  
  
  • موسوی, سیدابراهیم, منجذب, محمدرضا. (1399). مقاله پژوهشی: ارائه الگوی بهینه منابع و مصارف بانکی با تاکید بر نقش مدیریت ریسک( رویکرد معیار جامع و روش تسلسلی حداقل کردن بدون محدودیت). راهبرد مدیریت مالی, 8(2), 23-40.
  
  
  • خوش بین, رسول, رضایی, فرزین, رستگارسرخه, محمد علی. (1399). مدیریت‌ریسک‌نقدینگی در عملیات بازار باز بین‌بانکی با معیار GlueVaR. مهندسی مالی و مدیریت اوراق بهادار, 11(45), 199-222.
  
  
  • برادران حسن زاده، رسول ؛ حشمت، نساء. (1398). تاثیر سپرسرمایه بر ارتباط بین ریسک نقدینگی و ریسک پذیری بازاری و دفتری بانک ها، پژوهش های پولی بانکی، تابستان 1398، شماره 40، ص 197 تا 222.
  
  
  • پدرام، مهدی ؛ شیرین‌بخش، شمس‌الله ؛ زواریان، زهرا. (1388). پیش بینی جریان نقدینگی بانک به منظور تعیین شکاف نقدینگی ،دانش مالی تحلیل اوراق بهادار (مطالعات مالی) پاییز 1387، ص 1 تا 38.
  
  
  • زواریان، زهرا (1388). بینی وضعیت جریان نقدینگی بانک به منظور تعیین نیاز نقدینگی پیش . تهران.پایان نامه نامه کارشناسی ارشد. دانشگاه .الزهرا
  
  
  • Adao, Bernardino and Silva, Andre C., The Effect of Firm Cash Holdings on Monetary Policy (May 1, 2019). Available at SSRN: https://ssrn.com/abstract=2635727 or http://dx.doi.org/10.2139/ssrn.2635727
  
  
  
  


• Basel Committee on Banking Supervision (BCBS) (2008). Principles for sound liquidity risk management and supervision. September, No.144. Bank of International Settlements, available at http://www.bis.org/publ/bcbs144.pdf


• Basel Committee on Banking Supervision (BCBS) (2010). Basel III: International Framework for Liquidity Risk Measurement, Standards and Monitoring. December, No.188. Bank for International Settlements, available at http://www.bis.org/publ/bcbs188.pdf.


• Basel Committee on Banking Supervision (BCBS) (2011). Revisions to the Basel II Market Risk Framework. February, No.193. Bank for International Settlements, available at http://www.bis.org/publ/bcbs193.pdf.


• Basel Committee on Banking Supervision (BCBS) (2012). Basel III Liquidity Standard and Strategy for Assessing Implementation of Standards Endorsed by Group of Governors and Heads of Supervision. Press releases, 8 January 2012. Bank for International Settlements, available at http://www.bis.org/press/p120108.htm


• Basel Committee on Banking Supervision (BCBS) (2013), Basel III: The Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools. January, No.238. Bank for International Settlements, available at http://www.bis.org/publ/bcbs238.pdf


• Dlugosz, Jennifer and Gam, Yong Kyu and Gopalan, Radhakrishnan and Skrastins, Janis, Decision-Making Delegation in Banks (February 19, 2019). Available at SSRN: https://ssrn.com/abstract=3155683 or http://dx.doi.org/10.2139/ssrn.3155683


• 16-Francisco Salas-Molina, Fitting random cash management models to data, Computers and Operations Research (2018), doi:10.1016/j.cor.2018.04.007


• Madjid Tavana , Amir-Reza Abtahi , Debora Di Caprio ,Maryam Poortarigh ; An ArtiÞcial Neural Network and Bayesian Network Model for Liquidity Risk Assessment in Banking, Neurocomputing (2017), doi:10.1016/j.neucom.2017.11.034


• Olha M. BARTOSH; MANAGEMENT ACCOUNTING AS THE BASIS FOR EFFECTIVE SYSTEM OF BANKING MANAGEMENT, FINANCIAL SPACE,2014, № 1 (13)


• Patrick M. McGuire, BANK TIES AND BOND MARKET ACCESS: EVIDENCE ON INVESTMENT-CASH FLOW SENSITIVITY IN JAPAN, NATIONAL BUREAU OF ECONOMIC RESEARCH,1050 Massachusetts Avenue,Cambridge, MA 02138,April 2003


• Sound Proictices for Managing Liquidity in Banking Organizations, Basel Committee on Banking Supervision,BIS, February 2000


• Syed Quaid Ali Shah, , Muhammad Tahir, Imran Khan, Syed Sadaqat Ali Shah, Factors Affecting Liquidity of Banks: Empirical Evidence from the Banking Sector of Pakistan, Faculty of Management & FinanceUniversity of Colombo, June, 2018 ,Vol. 09, No. 01