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This research delves into the intricate dynamics of financial risks—specifically credit, market, and operational risks—within the banking, investment, and corporate sectors, with a focus on both global and Indonesian contexts. By examining the key factors contributing to credit risk, the impact of global market volatility on financial stability, an...
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... decisions are shown to have far-reaching implications for pricing strategies and competitive advantage, yet they come with their own set of risks that must be carefully managed. Based on Table 1 in this dataset encapsulates the core outcomes of 29 research papers, each delving into different facets of financial risk management, technological innovation, and other factors influencing the banking, investment, and corporate sectors. Below is a comprehensive explanation of some of the key findings highlighted in this column: ...Similar publications
This study investigates the extent to which operational risk control strategies influence employee performance of commercial banks in Cameroon. Primary data was obtained by administering close-ended questionnaires to respondents of 14 selected commercial banks in Cameroon. Data analysis was based on descriptive statistics and Ordinary Least Squares...
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... Financial management practices show extensive diversity in both formality and effectiveness when it comes to handling cash and receivables. The analysis demonstrates how minor enhancements in cash and credit management strategies bring major benefits for business liquidity and lower financial risks for entities operating in such environments (Nasimiyu, 2023;Vento & La Ganga, 2009;Akkizidis & Stagars, 2015;Wang, 2024;Siraj et al., 2024;Laghari et al., 2023). ...
The research examines the effects of cash flow management and receivables control on business liquidity at trading companies within Makassar Central Market. The research investigates how operational cash flow management together with receivable processes support financial reliability when informal credit systems exist in conjunction with restricted formal financial institution services. The researchers conducted quantitative surveys with 100 market trading companies to apply regression analysis on cash and receivables management effects on liquidity levels. Liquidity enhancement arises from effective cash management and especially from receivables management according to research findings. Businesses can decrease their reliance on outside financing sources while satisfying short-term debts by handling receivables effectively in order to receive cash flow improvements in a timely manner. The analysis identifies the necessity for financial management systems which fit specific situations encountered in informal markets that lack traditional accounting systems. The research contributes useful knowledge about liquidity management in small enterprises while providing valuable advice to trading companies which aim to develop their financial management methods. The study outlines prospective recommendations for improving market trader financial skills while expanding their access to official financial products.
... JPMorgan Chase utilized AIpowered software to examine millions of transactions automatically thus recognizing suspicious activity patterns. The system employs machine learning algorithms to detect unusual activity which helps decrease false alarms as well as cut down on financial losses stemming from fraudulent activities (Siraj et al., 2024). The improved fraud detection system proved profitable to JPMorgan Chase through both enhanced capability and reduced operational expenses. ...
Due to the rapid development of artificial intelligence (AI), banking and financial services have had a significant impact on financial and operational risk management. With the use of AI-powered technologies such as machine learning, deep learning, and natural language processing, financial institutions have been able to help improve their predictive capabilities, make decisions faster, and reduce risks better. This paper aims to provide a critical analysis of how AI is playing out in financial and operational risk management based on the theoretical foundations, applications, tools, ethical considerations, and legal implications, as well as the future trajectories of AI in the financial and operational risk management space. This study examines the possibilities of AI-driven solutions in predictive analytics, fraud detection, credit risk assessment, algorithmic trading, compliance monitoring, and operational risk management. AI systems can use large datasets to identify patterns, identify anomalies, and make data-driven predictions so they can enhance the accuracy of risk assessment and financial decisions by leveraging data. Furthermore, AI helps optimize the portfolio in a dynamic condition as the portfolio is updated based on the actual time conditions of the market. Yet, despite its potential for transformational impacts, AI also presents some ethical and legal challenges — data privacy, algorithmic bias, a lack of transparency, issues of regulatory compliance, and cybersecurity threats, among them. This paper shows the practical utilization of AI in financial institutes through real-world situations and the impact of AI on mitigating risk and enhancing efficiency and security. The study implies that AI would remain the critical factor in the development of financial risk management, turning out to be a nice thing for innovation and defending in a more intricate career. However, the study demonstrates that responsible deployment and long-term sustainability in financial operations depend, among other things, on regulatory oversight, ethical AI governance, and human-AI collaboration.
... The dynamics of the retail business are heavily influenced by changes in consumption patterns, intense competition, and fluctuating macroeconomic conditions. For instance, during the 2019-2023 period, many retail companies faced financial pressure due to the COVID-19 pandemic, forcing them to adjust their operational and financial strategies (Rizal S et al., 2024;Setiawan & Hutomo, 2023). Several retail companies listed on the Indonesia Stock Exchange (IDX) even underwent significant revisions in their financial reports, indicating potential declines in earnings quality due to economic uncertainty (Siregar et al., 2021). ...
Earnings quality is a crucial indicator that reflects the reliability of a company’s financial information. Retail companies listed on the Indonesia Stock Exchange (IDX) are expected to present financial reports with high-quality earnings to support accurate decision-making by both internal and external users. This study aims to analyze the impact of profit growth, capital structure, and liquidity on earnings quality in Indonesian retail companies during the 2019–2023 period. This research employs a quantitative approach using purposive sampling, resulting in a sample of 27 retail companies analyzed over five years. The data used are secondary data from corporate financial reports, and the analysis is conducted using multiple linear regression with the assistance of the Statistical Package for the Social Sciences (SPSS) software. The findings indicate that profit growth, capital structure, and liquidity have a significant positive effect on earnings quality. These results confirm that these three factors play a crucial role in generating more reliable earnings information. The novelty of this study lies in the simultaneous examination of the impact of profit growth, capital structure, and liquidity on earnings quality in the retail sector over the 2019–2023 period, which has not been extensively explored holistically in the context of Indonesia’s capital market. This research contributes to the development of social sciences, particularly in accounting and finance theories, specifically regarding the determinants of earnings quality. Additionally, it provides practical recommendations for company management in preparing more transparent and relevant financial reports. For investors, the findings of this study serve as a valuable reference in assessing a company’s financial health, helping to avoid potential investment losses caused by poor earnings quality.
... In agriculture, for instance, insurance covers physical losses due to natural disasters, while hedging helps mitigate the impact of fluctuating commodity prices, a common challenge in this sector. Studies by Rizal et al. (2024) show that this combined approach is efficient during times of crisis when market conditions are unpredictable and when both operational risks and financial market risks escalate simultaneously. The demand for comprehensive risk management strategies has surged as the global market continues to evolve (Mızrak, 2023). ...
Purpose: This study examines the role and effectiveness of combining insurance and hedging as complementary strategies for managing financing risk in volatile economic environments. By investigating how these two risk management tools work together, the study aims to provide insights into how companies can optimize their financial stability through integrated risk mitigation. Research Design and Methodology: A systematic literature review (SLR) was conducted to analyze recent research on insurance and hedging within financial risk management frameworks. The study evaluates empirical and theoretical sources to explore the synergy between these strategies and their applicability across different risk scenarios. Findings and Discussion: The findings reveal that insurance protects companies from direct operational risks, such as asset damage and unexpected losses, while hedging mitigates market volatility risks, including interest rate and commodity price fluctuations. The combined use of these tools offers a dual-layered approach, providing comprehensive protection against diverse financial risks. Additionally, the study highlights the importance of regulatory support in facilitating access to these instruments, strengthening corporate resilience and stakeholder confidence. Implications: This research contributes to theory and practice by enhancing understanding of dual-instrument risk management. For managers, the findings serve as a guide for selecting appropriate risk mitigation strategies and balancing cost-efficiency and stability. For policymakers, the study underscores the need for a supportive regulatory environment to implement these strategies effectively. Future research could explore sector-specific applications and long-term effects on corporate performance.
... Understanding types of financial risk is important to create effective risk management plans. The main classifications of financial risks are: market risk, credit risk, operational risk, liquidity risk, and reputational risk (Siraj et al., 2024). ...
The role of financial risk management is critical to the stability and growth of any organizations, while the dynamic nature of financial risks and the rapid growth of risk management techniques pose persistent challenges. The current study investigates the best approaches for managing financial risks, examining some of the limitations of traditional approaches while also examining the opportunities offered by new technologies. The value of the study comes from its contributions to organizational resilience, the ability to manage risk-return trade-offs, as well as for informing regulatory frameworks in an increasingly risk-aligned financial environment. This systematic review's major aim was a systematic review of the effectiveness of traditional finance risk management techniques and modern techniques, as well as their relevance in various industries, and their gaps. It also aimed to identify actionable steps for businesses and politicians to improve financial risk management practices. The researchers conducted a comprehensive search of several academic databases, including Scopus, Web of Science, and Google Scholar, to find studies published between 2000 and 2023. The search terms were quite broad such as "financial risk management," "risk mitigation strategies" and "AI-based tools." Based on the search, the final analysis consisted of 85 studies. The data were extracted and analyzed after adhering to thematic analysis and PRISMA guidance. The findings suggest that while traditional approaches, including hedging and diversifying, are relevant in a stable financial environment, they are not sufficient to manage more complex risks from a stable standpoint. The more modern tools, including financial derivatives and AI-based technologies, are more flexible to execute as needed with AI-base credit risk models decreasing non-performing loans by 20%, for example. There still remains problem related to the costs, regulatory issues, and cybersecurity. The study also highlighted gaps in the literature, including the need to focus more on long-term evaluations of AI tools and also more context specific strategies for emerging markets. In conclusion, this research highlights the importance of integrating both traditional and modern risk management strategies to effectively tackle diverse financial risks, advocating for standardized frameworks, technological innovation, and ESG integration to build organizational resilience.
Informational bottlenecks in complex systems represent a critical challenge, influencing the quality and timeliness of decision-making across financial and non-financial markets. This research investigates the origins and dynamics of informational bottlenecks, exploring their impact on system efficiency, resilience, and decision quality. By examining theoretical models and real-world case studies, the study identifies key mechanisms through which bottlenecks emerge, including data overload, communication inefficiencies, and cognitive biases. Additionally, it evaluates strategies for mitigating these bottlenecks through advanced analytics, machine learning, and organizational design interventions. Special attention is given to the role of technology in enabling effective data flow and enhancing decision-making processes. This interdisciplinary analysis spans financial markets, where rapid information processing is vital, and non-financial systems such as supply chains and healthcare networks, highlighting universal challenges and tailored solutions. The findings contribute to a deeper understanding of how informational bottlenecks disrupt system performance and propose actionable strategies for fostering robust and adaptive decision-making frameworks in complex environments.
