Next Article in Journal
Mapping Capital Ratios to Bank Lending Spreads: The Role of Efficiency and Asymmetry in Performance Indices
Previous Article in Journal
Cost–Benefit Analysis of International Financial Reporting Standard and Russian Accounting Standard Integration: What Does Comparability Cost?
Previous Article in Special Issue
Sovereign Credit Risk in Saudi Arabia, Morocco and Egypt
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations

1
Department of Finance and Banking, Faculty of Economics, University of Benghazi, Benghazi 1308, Libya
2
Department of Finance and Banking, Faculty of Business, Applied Science Private University, Amman 11937, Jordan
3
Department of Finance, School of Business, The University of Jordan, Aqaba 77110, Jordan
4
Department of Accounting, School of Business, The University of Jordan, Aqaba 77110, Jordan
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2024, 17(7), 288; https://doi.org/10.3390/jrfm17070288
Submission received: 31 May 2024 / Revised: 2 July 2024 / Accepted: 4 July 2024 / Published: 8 July 2024
(This article belongs to the Special Issue Emerging Issues in Economics, Finance and Business)

Abstract

The assessment of total risk-weighted assets (LTRWAs) in the banking sector is of the utmost importance. It serves as a critical component for regulatory compliance, risk management, and capital adequacy. By accurately assessing LTRWAs, banks can effectively meet regulatory requirements, efficiently allocate capital resources, and proactively manage risks. Moreover, the accurate assessment of LTRWAs supports performance evaluation and fosters investor confidence in the financial stability of banks. This study presents statistical analyses and machine learning methods to identify factors influencing LTRWAs. Data from Bahrain, Jordan, Qatar, the United Arab Emirates, and Yemen, spanning from 2010 to 2021, was utilized. Various statistical tests and models, including ordinary least squares, fixed effect, random effect, correlation, variance inflation factor, tolerance tests, and fintech models, were conducted. The results indicated significant impacts of the unemployment rate, inflation rate, natural logarithm of the loan-to-asset ratio, and natural logarithm of total assets on LTRWAs in regression models. The dataset was divided into a training group (90% of the data) and a testing group (10% of the data) to evaluate the predictive capabilities of various fintech models, including an adaptive network-based fuzzy inference system (ANFIS), a hybrid neural fuzzy inference system (HyFIS), a fuzzy system with the heuristic gradient descent (FS.HGD), and fuzzy inference rules with the descent method (FIR.DM) models. The selection of the optimal model is contingent upon assessing its performance according to specific error criteria. The HyFIS model outperformed others with lower errors in predicting LTRWAs. Independent t-tests confirmed statistically significant differences between original and predicted LTRWA for all models, with HyFIS showing closer predictions. This study provides valuable insights into LTRWA prediction using advanced statistical and machine learning techniques, based on a dataset from multiple countries and years.
Keywords: Islamic banks; neural network; total risk-weighted assets; financial technology Islamic banks; neural network; total risk-weighted assets; financial technology

Share and Cite

MDPI and ACS Style

Alzwi, A.S.; Jaber, J.J.; Rohuma, H.N.; Omari, R.A. Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations. J. Risk Financial Manag. 2024, 17, 288. https://doi.org/10.3390/jrfm17070288

AMA Style

Alzwi AS, Jaber JJ, Rohuma HN, Omari RA. Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations. Journal of Risk and Financial Management. 2024; 17(7):288. https://doi.org/10.3390/jrfm17070288

Chicago/Turabian Style

Alzwi, Asma S., Jamil J. Jaber, Hani Nuri Rohuma, and Rania Al Omari. 2024. "Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations" Journal of Risk and Financial Management 17, no. 7: 288. https://doi.org/10.3390/jrfm17070288

Article Metrics

Back to TopTop