Risk Management In Financial Institutions Research Articles

Gold Price Inflation and Its Implications for Financial Markets: Strategies for Mitigation

Abstract: Gold prices are among the most important indicators in the global financial market. The international gold price has continued to rise in recent years, repeatedly setting new records, accompanied by a boom in gold consumption and investment. The rapid rise in gold prices is due to a combination of factors. Supply and demand, economic conditions, and geopolitical events can lead to price fluctuations. Among them, economic conditions depend on monetary policy, inflation expectations, and market volatility, which may affect the trend of gold prices. The rise in gold prices has exacerbated market volatility and put higher demands on risk management for financial institutions. At the same time, the rise in gold prices has also affected the entire financial industry and changed traditional asset allocation strategies. This article examines the influence of increasing gold prices on the financial market and proposes potential preventive measures or solutions through a survey of literatures, theoretical analysis and logical reasoning. At the same time, this can help financial traders plan their funds reasonably, reduce the impact of inflation and currency devaluation, and better cope with changes in international trade and exchange rates, thus allowing the financial market to operate better.

Risk Management and Software Solutions in Financial Institutions: Move Towards Effective Implementation

Technological advancements and intensified regulatory pressures have led to a new level of the evolution of risk management in financial institutions. The entities have exposure to different categories of risks, such as credit risks, market risks, operational risks, and cybersecurity risks. The growing intensity of these factors makes proper risk management crucial for the stabilization and success of such enterprises. This report analyzes the best practices and software applications adopted by financial institutions for effective risk management. Improved risk identification, assessment, mitigation, and reporting within the organization would be through the integration of advanced analytics, AI, ML, and cloud computing. Based on this, the current paper assesses the part that these technologies play in contributing to compliance, cost savings, and better decision-making skills.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

Strategic risk management in financial institutions: Ensuring robust regulatory compliance

Strategic risk management in financial institutions is a critical component for ensuring robust regulatory compliance and maintaining financial stability. This review explores the multifaceted nature of strategic risk management and its importance in the dynamic regulatory landscape of the financial sector. It delves into the fundamental components of risk management, including risk identification, assessment, mitigation, and monitoring, highlighting how these processes help institutions navigate the complexities of regulatory requirements. The discussion encompasses various types of risks faced by financial institutions, such as credit, market, operational, liquidity, and compliance risks, illustrating the need for comprehensive risk management frameworks. The review also reviews key regulatory frameworks, including Basel III, the Dodd-Frank Act, and guidelines from the European Banking Authority, emphasizing their impact on capital requirements, liquidity standards, and governance expectations. A robust risk management framework integrates compliance efforts with business strategy, ensuring that institutions are not only adhering to regulatory mandates but also aligning their risk appetite and tolerance with their strategic objectives. The role of technology, particularly in data analytics, real-time risk monitoring, and cybersecurity, is examined as a crucial enabler for effective risk management and compliance. Best practices for enhancing regulatory compliance are outlined, including continuous monitoring, regular audits, and scenario analysis. Challenges such as evolving regulations, financial product complexity, and globalization are addressed, with recommendations for adaptive strategies and industry collaboration. Through case studies, the review provides insights into successful risk management implementations and lessons learned from compliance failures. The review underscores the importance of strategic risk management in fortifying regulatory compliance and suggests future trends, such as advanced AI and machine learning, which could further revolutionize the approach to risk management in financial institutions. Keywords: Strategic Risk, Financial Institution, Regulatory, Compliance

Does Managerial Overconfidence Change with Market Conditions? Risk Management for Financial Institutions

Overconfidence (hubris or overestimation of one’s ability to perform) has been viewed in the finance literature as a character trait that is stable over time, e.g., assuming that if a manager is overconfident, he/she is overconfident all the time. In this paper, we aim to show that managerial overconfidence can be state-contingent, i.e., the level of managerial overconfidence could be influenced by an external economic shock such as the global financial crisis in 2008. A novelty of this paper is to provide evidence for and application of the concept of state-based managerial overconfidence, which is new in the finance literature. Two empirical studies were reported. In the first study (Study 1), we analyzed real market data by linear regression. We found that managerial overconfidence could vary according to changes in the state of the macroeconomy or tightening of corporate governance policies. In the second study (Study 2), we conducted a lab experiment simulating how external manipulations could alter participants’ confidence level. Both our empirical studies provide strong evidence of state-contingent overconfidence by Student’s t-test and contribute to the current finance literature, which assumes overconfidence as a personality trait. Our findings have important practical implications for the credit market. According to the state-contingent overconfidence hypothesis, creditors might reduce the loan amount or the loan duration (or other loan contract terms) too excessively by more than the efficient level during an economic downturn if the offsetting effect of state-contingent overconfidence is ignored.

Possible Challenges and Recommendations for Risk Managers in Financial Institutions

The article analyses the importance and potential challenges of risk management in financial institutions, highlights the difficulties financial institutions face in identifying, assessing and mitigating the various risks within their organisations, and makes a number of recommendations to The article analyses the importance and potential challenges of risk management in financial institutions, highlights the difficulties financial institutions face in identifying, assessing and mitigating the various risks within their organisations, and makes a number of recommendations to address these issues.This paper explores these challenges, which include spillover effects, cybersecurity issues, efficiency trade-offs between costs and processes, regulatory compliance, and the impact of globalisation. Recommendations are made to address these challenges, such as adopting standardised procedures, embracing technological innovation, identifying emerging risks, and increasing automation of risk management processes. Effective risk management practices are essential to address the complexity of the financial industry and ensure organisational resilience.

Artificial intelligence and US financial institutions: Review of AI-assisted regulatory compliance for cybersecurity

As cyber threats and regulations become increasingly complex, financial institutions in the U.S. are in need of innovative cyber security solutions. This study examines the potential for artificial intelligence (AI) in addressing this problem. Artificial intelligence has significant potential for real-time threat detection, automated compliance processes, and proactive risk management. Nonetheless, ethical considerations, concerns about personal data privacy, and potential biases in AI algorithms require careful consideration. In light of this, the research proposes recommendations for developing innovative AI solutions. In addition, it addresses ethical and privacy concerns, as well as providing policy recommendations for the responsible and effective adoption of AI in the financial sector. This research highlights AI's potential to significantly enhance security and risk management for financial institutions. Additionally, it emphasizes responsible and ethical implementation.

Is the hybrid method more adequate for measuring operational risk?

Research aims: Risk management in financial institutions struggles with setting suitable capital charges for operational losses, resulting in large, disproportionate reserves that impact profits. This study, therefore, aims to develop a tailored operational risk measurement model for general takaful companies, addressing this challenge and optimizing capital allocation.Design/Methodology/Approach: This study employed a hybrid approach, merging the loss distribution approach (LDA) with historical data and scenario analysis for insurance company loss events. Compiling data into distributions, it utilized Monte Carlo simulations to determine value at risk (VaR). The resulting VaR guided the calculation of operational risk capital charges for future periods.Research findings: Measurement using the hybrid method could produce more adequate operational risk capital charges. These results confirm the acceptability of the VaR calculation and have been validated by the Kupic test.Theoretical contribution/Originality: This research offers a more comprehensive alternative method of measuring operational risk by combining historical company data with expert opinions, making it more likely to be practiced in the industry.Practitioner/Policy implication: The results of this study put forward an alternative, more suitable model for industry and regulators to measure operational risk management in general takaful companies.

PRMIA Hungary Chapter and the 2023 Research Conference

Financial risks are a constant challenge for business organizations, and failure to manage them properly often has unforeseen consequences, as many crises have shown. In recent years, the regulation of risk management in financial institutions has increasingly focused on optimizing specific metrics, while a holistic approach, a classic risk management attitude, is necessary to understand and address the challenges of the operation on time. This paper describes the main activities of the Hungarian Chapter of the Professional Risk Managers’ International Association (PRMIA), through the events of the past years. The focus is on the annual research conference of PRMIA Hungary, a selection of articles from which are included in this issue. In recent years, the spectrum of programs offered by the organization has developed and become more colorful, thanks to the voluntary work of the steering committee and the support of corporate partners and sponsors. Our aim is to connect and develop the risk management community, and we welcome all contributions.

Роль бухгалтерського обліку в управлінні ризиками на прикладі фінансових інститутів

The article discusses the role of accounting in identifying and managing risks, in particular on the example of financial institutions. The specifics of financial institutions and their risks, in particular credit, interest, liquidity and market risks, are studied. The article defines the role of accounting in assessing these risks, as well as in managing them through internal control, analysis of financial statements and development of risk management strategies. Specific examples and guidance are provided on how accountants can identify, assess, and minimize risk in financial institutions while ensuring compliance with norms and regulations. The aim of the study is to substantiate the need to involve specialists in the accounting and analytical profile in the process of making effective decisions on risk management of a financial entity. In modern conditions, financial institutions are in an unstable economic, political and technological environment, which leads to a significant increase in uncertainty. This creates a complex set of potential risks that, if mismanaged, can lead to losses, damage, or even destroy the company’s business model and reputation. One of the key competencies of accountants today is the ability to identify potential risks in time to avoid them or effectively manage them. The scientific novelty of the study lies in the disclosure of the expediency of involving accountants in the decision-making process and the development of a policy of integrated risk management of a financial entity in order to preserve its value. It is concluded that a professional accountant is important in risk management for financial institutions and the role of a proactive approach to risk management, in which accountants are key players, is emphasized. It is emphasized that integrated risk management should be considered as an important aspect of modern business, which helps organizations to effectively manage various types of risks, including financial, operational, reputational, etc. This approach allows companies to more accurately analyze, assess, and manage risks, minimizing potential losses and increasing the likelihood of achieving their goals. Integrated risk management involves the implementation of specific strategies, processes, instruments, and systems that are integrated into the company’s overall strategy and business processes. This approach also contributes to increased transparency and improved corporate governance, which in turn increases trust from investors, partners, and customers. In addition, integrated risk management enables companies to be more adaptable to changing market conditions and respond quickly to potential threats and opportunities.

Climate Change Risk Management in Financial Institutions: Strategies and Challenges

Climate Change Risk Management in Financial Institutions: Strategies and Challenges

Mapping the Financial Risk Management Landscape: A Bibliometric Analysis of Recent Trends and Influential Contributions

This bibliometric analysis explores recent trends and influential contributions in financial risk management, shedding light on thematic clusters, keyword occurrences, and highly cited works. The analysis encompasses a wide array of literature, ranging from the integration of risk considerations into broader corporate strategies to the impact of technological advancements on risk management practices. Thematic clusters reveal the interconnectedness of business practices, corporate social responsibility, and risk management, while keyword occurrences highlight dominant themes like financial performance and credit risk management. Highly cited works by notable authors provide foundational insights into risk management for financial institutions, emphasizing liquidity risk, corporate governance, and the integration of theoretical frameworks with practical applications. The synthesis of these findings offers a comprehensive understanding of the evolving landscape of financial risk management, guiding researchers, practitioners, and policymakers in navigating the complexities of risk in contemporary financial contexts.

RISK MANAGEMENT IN AFRICA'S FINANCIAL LANDSCAPE: A REVIEW

This review paper examines the critical role of risk management in Africa's financial landscape. It sets out to achieve three primary objectives: to explore the historical development of the financial sector in Africa, review the literature on risk management in financial institutions, and evaluate the effectiveness of regulatory measures in ensuring financial stability. The study uncovers key findings that underscore the growing significance of effective risk management practices in African financial institutions. It highlights adopting advanced technologies and regulatory harmonization as pivotal factors shaping risk management approaches. Additionally, the research underscores the importance of sustainable finance and the integration of Environmental, Social, and Governance (ESG) factors into risk assessments. These findings emphasize that risk management not only ensures financial stability but also contributes to sustainable and responsible financial practices. The broader implications of this study extend to economic stability, investor confidence, and cross-border cooperation. Effective risk management practices mitigate financial risks and support the ongoing regional integration efforts in Africa, such as the African Continental Free Trade Area (AfCFTA). Ultimately, the study underscores that the future prosperity of African economies is closely tied to embracing evolving risk management practices, fostering a resilient financial landscape in the region.
 Keywords: Risk Management, Financial Institutions, Africa, Financial Stability, Regulatory Framework, Sustainable Finance, Regulatory Compliance, Cybersecurity, Crisis Management, Data Privacy.

Special Issue of the `Journal of Risk Management in Financial Institutions`

Special Issue of the `Journal of Risk Management in Financial Institutions`

Effective risk management for financial institutions’ partnerships with FinTechs

The aim of this paper is to provide insights into the regulatory concerns and challenges faced by financial institutions when partnering with FinTech companies. The paper highlights the recent consent orders issued against well-known financial institutions for their FinTech relationships and risk management programme shortcomings, as well as how financial institution and FinTech collaboration can overcome these shortcomings. It acknowledges the frustration caused by the limited guidance provided by regulators on incorporating FinTech into financial institution operations. However, it encourages financial institutions to learn from the published consent orders and improve their risk management programmes. The paper also discusses the factors that need to be considered in FinTech partnerships, such as overborrowing, bias and discrimination in algorithms, and data privacy and reliability. It emphasises the importance of compliance professionals in analysing regulatory considerations early and creatively to meet future demands. Furthermore, the paper explores the changing landscape and increasing reliance on technology, highlighting areas that need to be considered in financial institution risk management programmes. It addresses concerns related to alternative credit models, bias and discrimination in algorithms, and data privacy and reliability. Overall, readers can expect to gain knowledge and insights into the regulatory concerns and challenges associated with FinTech partnerships, as well as practical actions and considerations to mitigate risks and ensure compliance.

Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study

In an effort to contribute a quantitative, objective and real-time tool to proactively and precisely manage the factors underlying and exacerbating operational risks, this pre-registered study executes the empirical methodology approved in the associated pre-registered report (Cornwell et al., 2023). The application of the Bayesian network-based approach to an Australian insurance company shows that integrating a financial institution's loss and operational data in this way can effectively model the probability of an operational loss event within its interconnected operational risk environment. Further insights and efficiencies are gained by modelling multiple operational loss events together, rather than in isolation. A novel two-module framework derived specifically for causal factors analysis from the resulting operational risk model helps to highlight the relative importance of causal factors, their collective effects and critical thresholds requiring proactivity. These insights derived from the framework are expected to be strategically valuable in helping an organisation design intentional and targeted controls for and monitoring of operational risks. Given existing knowledge of the improvements quantitative risk management tools make to risk management effectiveness and subsequently firm value, the enhanced risk management and the operational efficiencies this tool seeks to afford should ultimately contribute to driving financial performance and firm value.

Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach

The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Risk management in financial institutions and acceptable best practices

Risk management in financial institutions and acceptable best practices

Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report

To enable more proactive management of the underlying sources of operational risks in financial institutions, this pre-registered study seeks to improve traditional qualitative approaches to causal factors analysis. A Bayesian network-based approach is used to leverage both incident and operations data to model the probability of operational loss events. The approach is applied and empirically tested in a case study on an Australian insurance company. The outputs from the model go beyond simply identifying key risk drivers to offer risk managers a deeper understanding of how causal factors influence risk. Insights into the collective effects of causal factors, their relative importance and critical thresholds strategically inform more efficient and effective mitigation decisions, ultimately enhancing firm performance and value.