Business Model and Bank Risk in Indonesian Islamic Bank

Anggaredho, Panji Patra; Rokhim, Rofikoh

doi:10.21776/ub.apmba.2017.005.03.2

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…7, No. 2, April 2024 Risiko bank menurut berbagai penelitian juga dipengaruhi oleh model bisnis, dimana model bisnis yang lebih besar mengedepankan fee based income (dibandingkan pendapatan atas intermediasi) akan membuat risiko bank menjadi lebih kecil (Anggaredho & Rokhim, 2017). Sedangkan peneliti lainnya mengatakan bahwa model bisnis bank yang mengedepankan pendapatan bunga (dibandingkan pendapatan fee based income) akan membuat risiko bank menjadi lebih kecil (Ismaulina & Doumbouya, 2020).…”

Section: Scientific Journal Of Reflectionunclassified

Analisis Tingkat Risiko Bank Pada Bank Pembangunan Daerah Di Indonesia

Anggaredho,

Margie,

Sari

et al. 2024

SJR

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This study aims to analyze the level of risk in Regional Development Banks in Indonesia. This study intends to examine and map the risk of banks in BPD in Indonesia. In this analysis, the level of bank risk is measured using Z-Score (Z score). The data used in this study are panel data of observations from 26 Regional Development Banks in Indonesia over the past 20 years (2001-2021) which are divided into four clusters, namely the Sumatra Cluster, the Java, Bali and Nusa Tenggara Cluster, the Kalimantan Cluster and the Sulawesi and Eastern Indonesia Cluster. The data analysis technique used is using the Anova Test with the help of the SPSS program. The results of this study concluded that: 1) Bank risk in BPD Sumatra region has the same level of stability as BPD in Java, Bali & Nusa Tenggara. 2) Bank risk in BPD in Kalimantan region has the same level of stability as BPD in Sulawesi & Eastern Indonesia region.

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Section: Scientific Journal Of Reflectionunclassified

Analisis Tingkat Risiko Bank Pada Bank Pembangunan Daerah Di Indonesia

Anggaredho,

Margie,

Sari

et al. 2024

SJR

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“…Some other studies centred on the relationship between bank risk management and their performance by using panel data analysis, e.g. Anggaredho & Rokhim (2017), Fu & Heffernan (2006), Geng et al (2016) and Hryckiewicz & Kozlowski (2017). In more detail, Fu & Heffernan (2009) investigated liquidity risk and its effect on Chinese traditional banks.…”

Section: Rationale Of the Researchmentioning

confidence: 99%

“…Geng et al (2016) concentrated on interest rate risk and its influence on Chinese traditional banks. Hryckiewicz & Kozlowski (2017) focused more on the impacts of the financial crisis on banks from 65 countries, while Anggaredho & Rokhim (2017) only investigated Indonesian banks.…”

Section: Rationale Of the Researchmentioning

confidence: 99%

“…This research attempted to identify the impact of risk management on the performance of traditional banks and challenger banks/fintechs. It was noted that the importance of risk management and its impact on performance was studied quite a lot for traditional banks, such as Anggaredho & Rokhim, 2017;Bessis & O'Kelly, 2015;Fu & Heffernan, 2009;Geng et al, 2016 andNakashima, 2016. However, the fintechs' relationship between risk management and performance had only limited investigations.…”

Section: Conclusion For Each Research Questionmentioning

confidence: 99%

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Comparative bank financial risk management models in fintechs and challenger banks

Zhao¹

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Risk management in banking operations is a popular topic among researchers in the fields of management and banking. Due to developments in technology, research on financial technology has also become a hot topic. Banks and financial technology companies (fintechs) need to learn what risks impact their operations and how to manage these risks effectively. The aim of this study is to investigate the relationship between risk management and bank performance, compare it between traditional banks and challenger banks/fintechs, and make suggestions on how to improve performance by analysing historical data. This thesis adopts a mixed-method approach, estimating risk variables and their impacts on bank performance through panel data regression models (random-effects and generalised method of moments) and conducting case studies to contribute to knowledge in theory and practice. This research investigates the relationship between five main types of risks (credit risk, market risk, liquidity and capital risk, reputational risk, and operational risk) and bank performance measured by three variables (ROA, ROE and EPS). This study confirms the importance of risk management in bank performance. For example, credit risk variables show negative impacts on all banks, which suggests that reducing credit risks could increase bank performance. Market risk variables are complex with both positive and negative effects on bank performance. Thus, banks should keep market risk at a balanced level to receive better performance. Moreover, bank performance could be increased by increasing liquidity, capital and reputation as well as reducing debt, operational issues and costs. The contributions of this research include the enhancement of literature on the relationship between bank performance and risk management. Also, this research creates a greater awareness of risk management for challenger banks and fintechs. Moreover, it fills gaps in the literature by comparing results for traditional banks with those for challenger banks and fintechs. The results of this research offer new insights into risk management for both traditional banks and challenger banks and fintechs for iii academics and have the potential to assist traditional banks and challenger banks and fintechs in managing their risks and improving their efficiency in practice.

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Analyse the Effect of Islamic Derivatives during Financial Crises of 2008 Using Data Envelopment Analysis

Asst.

Abdul²

2020

J. Phys.: Conf. Ser.

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There is a very broad debate recently on the competence of Islamic financial derivatives in banks as a risk management tool better than traditional financial derivatives in conventional banks. The economic crisis that occurred in 2008 was a real measure to prove or deny this claim. In terms of bank performance, this research empirically compares Islamic and traditional banks, to evaluate the feasibility and efficiency of Islamic and traditional banks. The Data Envelopment Analysis (DEA) as statistical approach used in this research. The Kuwaiti banks were selected as a case study for the period 2006-2011 because it cover the period before and after the financial crisis of 2008. The data obtained from the Scope website, the annual accounts from banks, the IMF database, PhD studies and other tools. The research showed that Kuwait’s traditional banks were higher in efficiency and performance before the 2008 financial crisis, but the Islamic banks made a better performance afterwards. However, Islamic and traditional banks are subject to a comparable degree of risk, but different in nature; and the Islamic banks are less safe to financial shocks than conventional banks are. Since Islamic banks are unable to charge a fixed, standardized return and are unable to borrow from the financial market, Islamic banks may face more risk and unpredictable asset returns. The implication of these facts of the over-management modernisation of Islamic banks is important in order to reach a higher degree of efficiency. It will give Islamic banks an advantage over the traditional bank. For traditional banks, they need to invent specific strategies to reduce costs and retain their market position.

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Business Model and Bank Risk in Indonesian Islamic Bank

Cited by 3 publications

References 14 publications

Analisis Tingkat Risiko Bank Pada Bank Pembangunan Daerah Di Indonesia

Analisis Tingkat Risiko Bank Pada Bank Pembangunan Daerah Di Indonesia

Comparative bank financial risk management models in fintechs and challenger banks

Analyse the Effect of Islamic Derivatives during Financial Crises of 2008 Using Data Envelopment Analysis

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