Risk Management in TAKĀFUL

Abstract: This chapter provides an in-depth exploration of advanced strategies in financial risk management, specifically tailored to navigate the challenges posed by economic volatility, instability, and uncertainty. It covers the critical tools and techniques used by financial institutions and corporations to assess, mitigate, and manage various types of risks, including market, credit, liquidity, and operational risks. The chapter discusses the importance of both quantitative and qualitative risk assessment methods, such as Value at Risk (VaR), stress testing, scenario analysis, and expert judgment. It also delves into the use of financial derivatives for hedging, portfolio diversification strategies, and liquidity management practices. Additionally, the chapter examines the role of emerging technologies like artificial intelligence (AI), blockchain, and big data analytics in transforming risk management processes. Finally, it emphasizes the integration of sustainability into risk management frameworks, highlighting the importance of addressing environmental, social, and governance (ESG) risks in today’s volatile economic landscape. Keywords: Financial Risk Management, Economic Volatility, Market Risk, Credit Risk, Liquidity Risk, Operational Risk, Quantitative Risk Modeling, Value at Risk (VaR), Stress Testing, Scenario Analysis, Derivatives, Portfolio Diversification, Liquidity Planning, Contingency Funding Plans, Credit Default Swaps (CDS), Counterparty Risk, Crisis Management, Regulatory Compliance, Artificial Intelligence (AI), Blockchain, Big Data Analytics, ESG Risks, Sustainable Risk Management.

Risk management is a critical function in the banking sector, ensuring stability amidst fluctuating market conditions, credit defaults, and operational failures. In India, the banking system has faced increasing pressure due to growing non-performing assets (NPAs), rapid digitalization, and heightened exposure to global financial markets. This paper explores the various risk management practices adopted by Indian banks, focusing on key risks such as credit, operational, market, and liquidity risks. Credit risk, primarily driven by rising NPAs, is one of the most significant challenges faced by Indian banks, especially public sector institutions. The study investigates how banks have responded through improved credit appraisal systems, stressed asset management, and stringent monitoring mechanisms. The paper also examines operational risks, which have grown with the expansion of digital banking, requiring banks to invest heavily in cybersecurity and internal controls. Additionally, market risk, driven by global economic fluctuations, and liquidity risk due to asset-liability mismatches are evaluated. Regulatory frameworks, particularly those introduced by the Reserve Bank of India (RBI), play a vital role in shaping the risk management strategies of Indian banks. Key regulations like the Basel III norms, prudential NPA standards, and risk-based supervision (RBS) are analysed to understand their impact on improving the resilience of the banking sector. Despite the advancements in risk management strategies, challenges such as high NPAs, cyber threats, regulatory compliance, and global economic volatility continue to persist. Case studies of the IL&FS and Yes Bank crises are presented to underscore the importance of robust risk management practices.

The primary objective of this paper is to examine the market risk and liquidity risk management techniques and practices followed by the Indian scheduled commercial banks (SCB) consisting of public sector banks (PSBs) and private sector banks (PVSBs) for five years from 2016-17 to 2020-21. The other objective is to compare market and liquidity risk management practices between the public sector banks (PSBs) and private sector of banks (PVSBs). The other purpose of the study is to review the strategies adopted by the SCBs in risk management practices. To study the risk management practices, six largest banks each from PSBs and PVSBs are taken for sample study. This study finds that the SCBs are facing credit risk, market risk (Interest rate risk, foreign exchange risk, commodity price risk and equity price risk,) liquidity risk and operational risk. It also finds that the PSBs are better reporting and presenting their risk management practices in their annual reports than that of PVSBs in risk identification, risk assessment and risk analysis. The results indicate that there is no significant difference between the PVSBs and PSBs in the policies and practices of market risk and liquidity risk assessment, evaluation, monitoring risk controlling and risk taking. This paper will be relevant and value to those interested in research in the risk management banking industry. This is a descriptive research based on secondary data.

PurposeThe purpose of this study is to examine the relationship between corporate governance and risk management of Indonesian banks.Design/methodology/approachImplementation of good corporate governance is measured by good corporate governance composite rating, which is the result of bank's self-assessment. Bank risk managements are measured by market risk, credit risk, liquidity risk and operational risk.FindingsThe study results showed that good corporate governance implementation in Indonesia was able to influence bank risk. There were differences in credit risk, liquidity risk and operational risk in banks with different governance ratings, but not at market risk.Originality/valueThe effectiveness of risk management and good corporate governance implementation is needed to enable banks to identify problems early, to follow up on rapid improvements and to be more resilient to crises. This study is an analysis of the relationship between corporate governance and banks' risk management in Indonesia. In particular, risk management is measured by four risks: market risk, credit risk, liquidity risk and operation risk.

Telekom Malaysia Berhad: A Study of Relationship Between Performance (ROA) And Internal and External Factors

This study aims to assess the impact of risk management practices on the financial performance of banking sector companies listed on the Indonesia Stock Exchange (IDX) during the period 2021–2023. The banking industry faces various types of risks that can affect financial performance, including credit risk, market risk, liquidity risk, and operational risk. Effective risk management practices are essential for maintaining bank stability and ensuring sustainable profitability. This research employs a quantitative approach using multiple linear regression analysis. The sample consists of 12 banking companies selected through purposive sampling based on specific criteria, including the consistent publication of financial reports during the study period and the completeness of the required data. Data were collected from annual financial reports accessed through the IDX website and the official company websites. The analysis was conducted using SPSS software and included descriptive statistics, classical assumption tests (multicollinearity, normality, heteroskedasticity, and autocorrelation), and hypothesis testing (t-test, F-test, and coefficient of determination). The results indicate that, collectively, credit risk, market risk, liquidity risk, and operational risk have a significant effect on financial performance, with an F-value of 13.160 and a significance level of 0.000. Partially, only liquidity risk shows a significant negative effect on financial performance (t = ?2.308, sig. = 0.028), while credit risk (t = 1.288, sig. = 0.207), market risk (t = 1.409, sig. = 0.169), and operational risk (t = ?1.170, sig. = 0.251) do not have a significant individual effect.

The study investigates the role of International Basel Capital Regulation in taming the financial capital via improving risk management in banking. The study examines if capital adequacy ratios of commercial banks calculated under Basel Capital Accord reflect credit risk, market risk, operational risk, liquidity risk, and economic impact in Pakistan. The study employed dual methodology utilising both primary and secondary data. A Liker-scale questionnaire was administered in addition to deploying panel data approach. The results of the study show that Credit risk and operational risk along with the size of bank and bank profitability show significant impact on capital requirements of the commercial banks of Pakistan. Credit risk showing significant negative relationship with the capital adequacy ratio of the commercial banks of Pakistan.

This paper aims to study how banks carry out risk management and how to avoid systemic risks in the banking industry under the interest rate hike policy in the post-epidemic era. This paper divides bank risks into four main aspects to analyze the impact of sharp interest rate hikes on bank risk management in combination with the acquisition of Credit Suisse and the bankruptcy of Silicon Valley Bank. They are credit risk, liquidity risk, market risk (specifically interest rate risk), and systemic risk. After research, this paper finds that during the COVID-19 period, including interest rate hikes, governments have introduced corresponding policies for risk management. Moreover, when the market risk puts too much pressure on the bank, it will lead to credit risk and then liquidity risk, which may eventually trigger systemic risk under the joint effect. Given the current high inflation situation, the high level of interest rates will remain for a long time. Therefore, banks should do as much as possible to manage risks and reduce the possibility of triggering systemic risks.

The study investigates the nexus between risk management and performance of Habib Bank Limited using the annual financial time series data from 2005 to 2021. The dependent variable is the bank performance measured by using return on equity. The independent variables are the financial risks, which include credit risk, liquidity risk, and operational risk. The findings suggest that the credit risk and liquidity risk negatively impact the performance of Habib Bank Limited, which means that the increase in credit risk and liquidity risk would result in declining the profitability of Habib Bank Limited. Moreover, operational risk has a significant positive effect on the bank's performance. The study concludes that Habib Bank Limited needs to control its credit and liquidity risks to perform well in the market. It is recommended that Habib Bank Limited adopt strict policies and do a risk analysis of each borrower before issuing the loan. Habib Bank Limited must offer attractive financial products to motivate the depositors to deposit more in the account to control the liquidity risk.

This study examines the hierarchical structure of risks influencing the financial distress of banks using the Interpretive Structural Modeling (ISM) method. Financial distress, regarded as the stage preceding bankruptcy, arises from the complex interaction of internal and external factors affecting banks and has far-reaching implications for financial stability and economic growth. In this regard, key risk components—including liquidity risk, credit risk, market risk, and operational risk—were first identified, and their interrelationships were analyzed using ISM. The research findings revealed that the ratio of net profit to total bank assets and the ratio of net working capital to total bank assets, with impact coefficients of 99% and 94%, respectively, play the most significant roles in reducing the financial distress risk of listed banks. Furthermore, market risk, the market-to-book value ratio, and the total debt-to-total assets ratio, each with an impact coefficient of 91%, showed a similar effect in mitigating this risk. Other variables, such as operational risk, interest rate risk, net profit to equity, and total asset size, also demonstrated approximately 80% influence and ranked in subsequent positions. The power–dependence analysis showed that liquidity risk, with 100% driving power and 17% dependence, is positioned in the independent zone, whereas credit risk and operational risk, characterized by high driving power and medium dependence, are situated in the linkage zone. Variables such as net profit, working capital, and market risk, with 100% dependence but low driving power, are placed in the dependent zone.

The research aimed to assess the impact of financial risk on the performance of cooperatives in Nepal, focusing on credit, operational, and market risks are independent variables and financial performance is the dependent variable. The Research data was collected from financial statements and regulatory records spanning mid-July, 2015 to mid-July, 2024, though the dataset was unbalanced due to variations in the licensing years of the cooperatives. Descriptive statistics, including mean, standard deviation, and range, summarized the data, while correlation coefficient analysis examined the strength and direction of relationships between variables. Regression analysis explored the causal impact of financial risks on cooperative performance. The findings revealed that credit risk had a marginal but its positive effect on financial performance, operational risk had a significant and positive influence, and market risk demonstrated a substantial positive impact. The study underscores the importance of comprehensive risk management practices and recommends that cooperatives implement strategies addressing all dimensions of financial risk, including the recruitment of skilled risk management professionals, to enhance overall performance.

This paper explores the critical role of the banking sector in the economy and society, with a focus on various risk dimensions affecting this industry. These dimensions include liquidity risk, credit risk, market risk, and systemic risk. The paper underscores the banking sector's significance for the national economy and financial stability, defining and explaining these risks' impact. The liquidity risk section delves into the definition, causes, and implications of liquidity risk, examining policies and measures taken by banks, especially in response to the challenges posed by the COVID-19 pandemic. Credit risk is explored through a case study of Credit Suisse, discussing the definition and consequences of credit risk and its potential role in bank bankruptcies. Market risk is analyzed with a focus on interest rate changes and their effects on banks, using the case of Silicon Valley Bank to illustrate how market risk can lead to bank failures. Systemic risk is explained, emphasizing its definition, causes, and significance to banks while also suggesting measures to avoid it. This paper concludes by summarizing the impact of these risks on the banking sector and offers recommendations for risk management and the future direction of the industry. Through case studies, it highlights the complexity of these risk dimensions and their crucial role in maintaining banking sector stability and overall economic well-being.

- This study aimed to evaluate the influence of market risk, credit risk, liquidity risk, and operational risk on the profitability of banks listed on the Indonesia Stock Exchange during the period from 2020 to 2022. The chosen timeframe coincided with the emergence of the Covid-19 pandemic, which naturally resulted in a decline in banking profitability and an increase in the risks faced by these banks. The research adopts a quantitative approach, and sample selection employed the purposive sampling method. Secondary data from the Indonesia Stock Exchange were utilized for analysis. Data processing was conducted using EViews 12, involving various tests such as descriptive statistical analysis, classical assumption testing, panel data regression analysis, model testing, and hypothesis testing. The findings of the study reveal that market risk positively affects profitability, while credit risk and operational risk has a negative impact on profitability. Conversely, liquidity risk does not demonstrate a significant effect on profitability. The outcomes of this study hold significance for academic researchers exploring the relationship between risk management practices and bank profitability.Keywords: Bank, Profitability, Risk

In light of the fact that sustainable development and financial stability are now essential to global social and economic progress. Thus, the current study looks into how risk management affects Ethiopian banks’ financial stability between 2017 and 2024 using the generalized moment (GMM) model. The study used financial stability as the dependent variable and liquidity risk, credit risk and operational risk as the independent variables. Moreover, the study used inflation GDP and exchange rate as the control variable. In light of this, the study found that the financial stability of Ethiopia’s commercial banks is significantly impacted negatively by credit risk, inflation, and exchange rates. Whereas GDP and liquidity risk have a significant and positive impact on the financial stability of Ethiopia’s banking industry, apart from them, operational risk doesn’t affect the financial stability of banks in Ethiopia. Thus, the study concluded that macroeconomic features (GDP, inflation, and exchange rate) and risk management factors (credit risk and liquidity risk) significantly affected the financial stability of banks in Ethiopia. Therefore, bank management should establish effective risk management mechanisms like ensuring secure loan granting and, improving the quality of credit appraisal and analysis and, collecting and updating information about customers in a timely and accurate manner, analyzing the risks of the loan plan to provide the expected risks and the bank’s controllability.

With the evolution of technology come along risks. With complex prototypes and methods, not only the decision-making propensity of the machines increases but also the risk assessment reduces and frauds increase. This calls for a better and improved version of risk analysis in the industry. Machine learning is considered an appropriate solution for the management of risks as it can produce the desired solution with less human effort. Risk is the probability of occurrence of failure of the system, which is unwanted in any project as it reduces the profit as well productivity of the system. So, to minimize the possibility of risks, certain methods are adopted, which operate through machine learning. Risk analysis mainly includes hazard identification, risk communication, risk assessment, and risk management. The chapter provides an insight into various applications of machine learning techniques in the field of risk analysis, which includes liquidity risk, operational risk, market risk, and RegTech. The application of machine learning in this sector has led to the conclusion that these methods or techniques can be used to analyze huge amounts of data with efficient predictive analysis. Various use cases have also been discussed such as supervision of conduct and market abuse in trading, fraud detection, and credit risk. Moreover, the future of machine learning in risk analysis and management is presented as well bringing out a positive picture. The chapter concludes with some limitations, which need to be fixed for better risk management.