Public Sector Accounting and Budgeting

Public Sector Accounting and Budgeting

Investigating the effects of financial engineering and control systems on the financial performance of banks with the approach of machine learning and spatial neural network

Document Type : Original Article

Authors
Wisam Fadhil Hanoon 1, 2
Parviz Piri 3
Ali Ashtab 4
1 Ph.D. Student of Accounting Department, Faculty of Economics and Management, Urmia University, Iran
2 Iraq
3 Associate Professor of Accounting,, Faculty of Economics and Management, Urmia University, Iran
4 Assistant Professor of Accounting,, Faculty of Economics and Management, Urmia University, Iran
10.22034/psab.2024.207728
Abstract
Objective: This study examines the factors affecting the financial performance of banks in Iran and Iraq and provides policy suggestions to improve this performance.
Method: This research has been investigated using neural network, machine learning, and spatial econometrics methods. The statistical population includes commercial banks in Iran and Iraq in the period from 2010 to 2023 for 44 Iraqi banks and 22 Iranian banks.
Research findings: The results show that factors such as asset return ratio, equity return ratio, asset quality, capital adequacy, liquidity management and operational efficiency have a significant impact on the financial performance of banks. Banks with better risk management, higher capital adequacy ratio and greater operational efficiency generally show better financial performance.
Conclusion: Improving the financial performance of banks requires comprehensive measures in various fields, including strengthening risk management, increasing capital adequacy, improving liquidity management, and increasing operational efficiency. Monetary authorities can help improve the financial performance of banks and strengthen the stability of the financial system by adopting appropriate policies and establishing strong regulatory frameworks.
Keywords
Keywords: bank financial performance
neural networks
machine learning

Subjects

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Public Sector Accounting and Budgeting
Volume 5, Issue 3 - Serial Number 17
August 2024
Pages 134-163

  • Receive Date 29 July 2024
  • Revise Date 26 September 2024
  • Accept Date 13 October 2024