Risk Management Model Research Articles

FITTING DYNAMICALLY CONSISTENT FORWARD RATE CURVES: ALGORITHM AND COMPARISON

This paper introduces a new methodology for estimating dynamically consistent forward rate curves, which are essential for obtaining arbitrage-free valuation and risk management models. Using U.S. Treasury data from January 2013 to June 2023, we fit dynamically consistent forward rates curves and test the goodness of fit between theoretical and observed prices. We compare the traditional static forward rate curve parameterizations (including Nelson–Siegel, Svensson, and cubic splines) with our new dynamically consistent methodology. The dynamically consistent forward rate curve estimates generally outperform the traditional static methods in reproducing market prices.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

Risk parity portfolio optimization under heavy‐tailed returns and dynamic correlations

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management.

Research on Issues and Strategies of Financial Management Informationization in the Era of Big Data

In the context of rapidly evolving big data and cloud computing technologies, the field of corporate financial management is undergoing a profound transformation. Financial management informationization, as a core engine driving improvements in corporate efficiency and cost optimization, has become a new frontier in industry competition. This trend not only accelerates the intelligent and real-time nature of corporate decision-making processes but also profoundly alters traditional models of capital operations, cost control, and risk management. However, while the wave of big data offers unprecedented data treasures and powerful analytical tools for corporate financial management, it also brings a series of pressing challenges that need to be addressed. This paper aims to thoroughly analyze the main obstacles faced by financial management informationization in the era of big data and to propose a series of practical solutions.

Devising a project risk management method under scrum conditions based on cognitive approach

Any project implemented using Scrum is characterized by the impact of risks, including negative changes in the environment and crisis circumstances. Therefore, the processes related to risk management, which is the object of this paper, become important. The problem solved in this study is to improve the efficiency of projects through the construction of a long-term strategy for reducing the level of risk and avoiding negative consequences for projects in the context of Scrum. The proposed method of risk management has been developed on the basis of the application of the synthesis of management of intelligent decision-making technologies and formalized methods. Difficult external circumstances are characterized by a high degree of uncertainty and do not always contribute to the successful implementation of the project. Therefore, this method of project risk management under Scrum conditions based on a cognitive approach is characterized by a combined combination of cognitive analysis, mathematical modeling, and expert methods. As part of the method, a model of project risk management under Scrum conditions has been built in the form of a fuzzy cognitive map, which could ensure determining the optimal strategic decision in dynamics, taking into account the effects of various factors. The result of applying this method is compliance with time limits, reduction of overspending of resources and losses in the project, as well as adaptation to rapidly changing circumstances and adequate response. The method of project risk management is characterized by solving the problem of formalizing management decision-making procedures and their information support, taking into account the availability of both quantitative and qualitative data. Within the framework of this method, a project risk management procedure under Scrum conditions has been proposed, which contributes to the systematization, monitoring, and control of risks under the conditions of complex, rapidly changing crisis circumstances

A multicriteria decision-making model for risk management in an integrated management system

PurposeThis paper proposes a multicriteria model for risk management to identify and assess risks associated with an integrated management system (IMS). The main benefit of the proposed model is its systemic and logical visualization, which may facilitate the understanding of this proposal’s practical application.Design/methodology/approachThe research design consists of four stages: (1) conduct a literature review to establish risk management models in IMS; (2) collect data concerning risk management models in IMS from a large multinational automotive company; (3) propose a multicriteria model to define and assess risks as well as prioritize mitigation actions and (4) apply the proposed multicriteria model to the data collected in case-based research to evaluate the practical viability of the model to contribute to methods traditionally used.FindingsThe results showed that the proposed risk management model contributes to more reliable decision-making in an IMS. The application of the proposed model identified 85 risks in the total processes of the IMS, 31 of which were classified as high risk; thus, priority actions to be taken were defined. The risk classification and prioritization facilitated the implementation of measures to mitigate or eliminate risks, as pointed out by the company managers.Research limitations/implicationsOne of the limitations is the fact that specific knowledge is required to maintain and update the multicriteria decision-making tool used in this study. Another one implies the approach to managing risks under the different ISO standards and sector-specific requirements, since this may require updates and customization of the proposed risk management model.Practical implicationsThe implementation of IMS in contemporary business environments can be supported by a robust risk management approach. In addition, it provides the leadership with a holistic view of multiple aspects related to a company and fosters continuous improvement.Social implicationsThe social implications of this study are assessed indirectly. This study contributes to the improvement of company management models.Originality/valueTraditionally, the methods used for risk management in IMS are usually applied independently of techniques such as failure mode and effect analysis. The model developed in this work enables to manage risks continuously to achieve a systemic view of organizational issues and greater transparency of the processes.

Building credibility for human systems integration in model‐based systems engineering

AbstractRobust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models.

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.

Estimating probability of default via delinquencies? Evidence from European P2P lending market

The unprecedented growth of the financial sector's digital transformation opens wide areas to the scaling up of finance in innovative and knowledge-based projects. Improving risk management takes centre stage in the acceleration of this process. This study uses loan-book data from the peer-to-peer (P2P) lending market to empirically investigate the determinants of default risk. Using the loan-book database covering the period from 2014 to 2020, we examine multiple factors related to the default risk of loans issued by P2P lending platforms. The results indicate that a higher interest rate and higher stock market returns increase the probability of default in the P2P lending market. Results are robust to additional tests based on endogeneity correction, the LASSO method and sampling bias. The severity of the impact of market returns and interest rates is found to be significantly different based on the levels of financial technology (FinTech) adoption and banking sector distress. Increases in the market interest rate are found to boost the sensitivity of P2P loan defaults to stock market volatility. This study contributes to existing literature on risk management models with its consideration of country-specific factors, paving the way to future best practices in the market.

Generalized Modular Method of Project Risk Management

The article discusses risk management models and frameworks implemented during the development of multi-user cross-platform distributed IT projects. For this class of pro-jects, the use of quantitative risk assessment methods, rules for absorbing and adding risks, which make it pos¬sible to systematically assess the likelihood of negative events and their impact on each stage of the project, are observed. Authors propose a method of modular risk management, present the main indicators for assessing the progress of the implementation of the i-th module and its operational ma¬nagement, and provide a formula for calculating project efficiency. These calculated indicators can be used to estimate the cost of resources at a certain point in time, estimate the timeframe for completing the module, and decide on the feasibility of further development.

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Developing a data-driven multimodal injury and illness prevention programme in male professional football based on a risk management model: the IP2 NetWork

BackgroundCurrent injury prevention programmes in football are limited by a one-size-fits-all approach, which predominantly focuses on preventive exercise programmes while ignoring differences in risk profiles between individuals and teams.ObjectiveTo address...

Model risk management in stress testing: The road up to here

This paper reviews the historical evolution of the quantitative models, model usage and model risk management (MRM) in large US banks since the 1980s. It comments on the most significant model-related events through this period. Some of these events were associated with contexts of great stress to the financial system. The paper identifies the main features of modelling in finance and economics that distinguish it from its application in the natural sciences. Then it presents the different types of models used in the large banks' stress testing programmes, the specific characteristics and risks of each type of model and the best practices for the implementation of sound model risk management, and it suggests a proper way to interpret the results of stress testing and capital assessment in the presence of model risks. The paper discusses the future evolution of MRM towards the implementation of a `fully fledged risk management framework` along the lines of other risk management disciplines, including risk identification, measurement, monitoring, reporting, limiting and capitalisation. It acknowledges the enhancements and opportunities offered by advances in the use of artificial intelligence and machine learning as well as increases in computational power. The paper concludes by noting that the model risk management framework that has been enforced by the Federal Reserve's stress testing and capital planning programmes has substantially strengthened the capital position, profitability and resilience of the large banks in the US.

Risk management in international supply chains: geopolitical and geoeconomic dimensions

In the current climate of global instability driven by dramatic geopolitical and geoeconomic transformations, international supply chains face numerous risks that significantly impact the continuity and efficiency of logistics processes. Threats such as military conflicts, port blockades, border instability, climate changes, and cyber threats introduce new challenges for companies, particularly Ukrainian ones, which require adaptive strategies to ensure the resilience of their supply chains. The purpose of this study is to systematize key geopolitical and geoeconomic risks affecting international supply chains and to identify optimal approaches to managing these risks under conditions of instability, especially for Ukrainian companies facing heightened threats. The study includes an analysis of global risks based on international scientific literature, specific risks in Ukraine and their impact on import, export, and transit chains, and the development of a generalized risk management model tailored to Ukrainian enterprises. The research identified effective risk management methods such as supply chain diversification, the creation of alternative routes, and the use of insurance mechanisms. A practical case study on risk management in the import of industrial fasteners illustrates the stages of collaboration with forwarders and carriers, partner selection, and response to disruptions, demonstrating approaches that can be used to minimize the impact of risks. The findings provide practical recommendations for Ukrainian companies, highlighting an adaptive approach to risk management that will help them maintain competitiveness and ensure supply chain resilience in a rapidly changing global environment. Future research should focus on integrating digital technologies, such as blockchain, artificial intelligence, and big data analytics, to strengthen the resilience and flexibility of international supply chains.

A governance and risk management framework for project management in the oil and gas industry

The oil and gas industry operates in an environment marked by high risks, complex regulations, and significant financial investments. Effective project management in this sector requires a robust governance and risk management framework to address operational, regulatory, financial, and environmental challenges. This review proposes a comprehensive governance and risk management framework tailored specifically to the unique needs of oil and gas projects. The framework integrates governance structures that define clear roles, responsibilities, and decision-making processes, ensuring that projects align with corporate objectives and compliance standards. Central to this framework is a risk management model that identifies, assesses, and mitigates potential project risks. The model emphasizes continuous risk monitoring, utilizing advanced technologies such as predictive analytics, AI, and digital twins to forecast risks and optimize decision-making. Additionally, it advocates for proactive stakeholder engagement strategies to ensure alignment among diverse stakeholders, including government agencies, investors, and local communities. The governance aspect of the framework promotes transparency, accountability, and sustainability, ensuring that projects meet both operational goals and regulatory requirements. It also introduces agile project management techniques to enhance flexibility and responsiveness throughout the project lifecycle. By integrating technological tools like blockchain for contract management and AI for predictive maintenance, the framework addresses both governance and risk management challenges. Ultimately, the review serves as a guide for oil and gas companies to enhance project efficiency, minimize risks, and meet the demands of an increasingly complex industry landscape.

The process of dangerous event management taking into account economic, environmental and occupational losses

Aim of the research is in risk management process development of a dangerous event taking into account economic, environmental and professional losses.Materials and methods. To develop the process of risks management from various dangers through the integration of their losses (economic, environmental, life and health of employees), we take the most common model "bow tie", which allows to establish a cause and effect relationship between danger – a dangerous event and the severity of the consequences.Results. As a result of the research, it was found that each hazard must be considered based on three different types of damage. This makes it possible to implement another mechanism for identifying the most significant dangerous factors that lead to significant general economic losses. In the case of risk acceptability from each individual dangerous factor, there is an additional opportunity to analyze them based on the relationship between financial activity, economic and professional losses. The second consists in determining the limits of the acceptability of risks, which are formed not only based on the total acceptable economic losses, but also taking into account the stability of the enterprise's work. This implies the need to actively invest in new technologies based on short-term and long-term perspectives. The main types of dangers are defined in the risk register, which is developed by the organization to determine the integration of dangers consisting of natural, technogenic, environmental, occupational and economic groups of dangers and dangerous factors. The authors develop the model and algorithm of risk management based on the combination of different consequences of dangers in economic, environmental character, life loss and health.Scientific relevance. The article determines the relationship between the probability of a dangerous event and the severity of different types of consequences – economic, environmental and occupational dangers through the analysis of Euler-Venn diagrams.Practical relevance. The authors develop the basic principles of risk management of different types of losses: economic, environmental ones, loss of life and health of employees.

Research on the Impact of Digital Financial Technology on the Business Model Transformation of Commercial Banks

This paper explores in depth the broad impact of digital financial technologies on the business models of commercial banks and analyzes in detail the transformation strategies that commercial banks should adopt in response to these changes. With the rapid development of cutting-edge technologies such as big data, cloud computing, artificial intelligence, and blockchain, digital finance has become an irreversible trend in the financial industry, which has not only changed the way financial services are provided, but also reshaped the market competition pattern of commercial banks. This paper first expounds the impact of digital financial technology on the traditional business model of commercial banks, including the reduction of market entry threshold, the change of competition focus and the innovation of risk management model. Then, the paper focuses on the specific strategies of commercial bank business model transformation, including technological innovation, organizational structure optimization, process management improvement, talent training and introduction, and strengthening cooperation and win-win situation. Through the introduction of the actual case analysis, it shows the successful practice and lessons of commercial banks in the business model transformation. Finally, this paper concludes that the key to the successful transformation of commercial banks' business model lies in the deep integration of technology and business, as well as the ability to flexibly respond to market changes and customer needs. Only in this way can commercial banks remain competitive and achieve sustainable development in the era of digital finance.

The Impact of Quantum Computing on Financial Risk Management: A Business Perspective

The rapid advancement of quantum computing is poised to revolutionize industries, particularly in areas requiring complex computational power. This paper explores the potential impact of quantum computing on financial risk management from a business perspective. Traditional risk management models struggle to cope with the increasing complexity of global financial systems, making quantum computing a promising solution. By leveraging quantum algorithms, financial institutions can optimize risk assessment, enhance portfolio management, and significantly improve predictive models. This paper examines key algorithms and their applications in finance, discusses the challenges and opportunities in adopting quantum solutions, and presents case studies of early adopters. The findings suggest that quantum computing can offer a competitive advantage to businesses by providing superior risk analysis capabilities. However, limitations in technology, infrastructure, and expertise remain significant hurdles. This study contributes to the growing discourse on quantum computing by providing actionable insights for businesses looking to integrate quantum solutions into their risk management strategies.

Research on Enterprise Financial Information Risk Identification and Dynamic Control Management Model Based on Genetic Algorithm

In the contemporary global economic landscape, the imperative for enterprise financial informatization as a catalyst for efficiency, cost reduction, and innovation is evident. However, the deepening integration of informatization introduces corresponding risks, necessitating the establishment of effective risk management systems. This paper proposes a genetic algorithm-based model for enterprise financial informatization risk management, aiming to offer precise and actionable solutions. The study integrates genetic algorithms to enhance the adaptability and flexibility of the risk management model in the dynamic information environment. Through in-depth research on corporate financial informatization risks, a multi-dimensional risk management control model is constructed, considering technical, organizational, and environmental factors. The genetic algorithm introduces a new perspective for model optimization, enabling efficient search and optimization capabilities. The model not only identifies but also controls and governs risks, providing timely intervention and effective risk management. Moreover, the genetic algorithm facilitates intelligent decision support for risk management by adapting to changing environments. Empirical analysis validates the model’s feasibility and effectiveness in real enterprise cases, emphasizing its practicality.