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Abaker, Abdelgalal O. I.
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Analysis of the Factors Affecting the Financial Performance of Insurance Companies using Statistical Modeling Younis A., Halima; Salem, Hamdy Mohamed; Alsanea, Mahmoud Selim; Elemam, Halla Z. S.; Abaker, Abdelgalal O. I.
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4217

Abstract

The insurance industry is fundamental to the global economy, accounting for about 7% of the gross domestic product (GDP) in numerous developed nations and serving a crucial function in risk management and financial stability. Recent years have seen escalating economic pressures that have adversely affected the profitability of insurance firms. These Difficulties encompass escalating inflation rates, a surge in claims, and losses attributable to natural disasters, with swings in interest rates that have impacted investment returns and the valuation of financial portfolios. This study aims to examine the determinants influencing the financial performance of insurance businesses through precise statistical models, with a particular emphasis on return on equity (ROE) as a principal metric. The research utilized real-time data encompassing characteristics such as insurance density, interest rates, underwriting capacity, and insurance expenditures, among others. Statistical modeling was employed to ensure the degree to which these factors influence profitability. The project seeks to establish an analytical framework to improve the efficiency of underwriting and pricing decisions. It further advances academic literature by utilizing sophisticated analytical tools to understand profitability dynamics inside the insurance sector.
Machine Learning and Statistical Model Hybrid Approach to Optimizing Financial Data Prediction Elsheikh, Babikir M. O.; Abdo, Ahmed Edris; Hamid, Nagat; Tayfor, Abdelsamie Eltayeb; Abaker, Abdelgalal O. I.; Hussin, Hiba A. A. A.
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4630

Abstract

Accurate price volatility prediction is a cornerstone of sound investment decisions and effective dynamic risk management in financial markets. This study addresses a significant research gap: the limited number of studies exploring the systematic integration of traditional statistical models and artificial intelligence techniques within emerging financial markets, despite their high levels of instability and volatility. The research aims to develop a hybrid predictive framework that combines the flexibility of linear models, specifically ARIMA, with the ability of machine learning algorithms to grasp the complex, nonlinear patterns inherent in financial time series. Furthermore, the study highlights an application gap: the underutilization of advanced volatility estimators. The Garman-Class estimator was adopted as a more efficient and accurate alternative to traditional estimators for measuring daily volatility, due to its reliance on four-part price information (open, close, high, and low). The proposed framework was applied to data from Savola Group, listed on the TASI. The results demonstrated the superiority of the proposed hybrid model in improving forecast accuracy and reducing predictive error measures, particularly the MAE, and RMSE, compared to traditional single-model models. The scientific value of this research lies in its contribution to bridging the knowledge gap related to the integration of statistical models and artificial intelligence techniques in the emerging markets environment. Furthermore, it provides an advanced analytical tool that can enhance asset allocation efficiency and support decision-makers and portfolio managers in navigating the dynamics of highly volatile markets.
Co-Authors Abdo, Ahmed Edris Alsanea, Mahmoud Selim Elemam, Halla Z. S. Elsheikh, Babikir M. O. Hamid, Nagat Hussin, Hiba A. A. A. Salem, Hamdy Mohamed Tayfor, Abdelsamie Eltayeb Younis A., Halima