Risk Management Model Research Articles

Artificial Intelligence: new data and new models in credit risk management

During the last decade, the increase in computational capacity, the consolidation of new data processing methodologies and the availability of access to new information concerning both individuals and organizations, aided by the widespread internet usage, has increased the development and implementation of artificial intelligence (AI) within companies. The application of AI techniques in the banking sector attracts wide interest as the extraction of information from data is inherent to banks. As matter of fact, for many years now models play a crucial role in several banks processes and are strictly regulated when they drive capital measurement processes. Among banks’ risk models a special role is played by credit ones, as they manage the most relevant risk banks face and are often used in regulatory relevant processes. The new AI techniques, coupled with the usage of novel data, mostly unstructured ones related to borrowers’ behaviors, allow for an improvement of the accuracy of credit risk models, that so far relied on structured internal and external data. This paper takes inspiration from the Position Paper Aifirm 33/2022 and its English published translation (Locatelli, Pepe, Salis (eds), 2022. The paper is focused on literature review regarding the most common AI models in use in credit risk management, also adding a regulatory perspective due to the specific regime banking models are subject when they are used for regulatory purposes. Furthermore, the exploration of forthcoming challenges and future advancements considers a managerial perspective. It aims to uncover how credit risk managers can leverage the new AI toolbox and novel data to enhance the credit risk models’ predictive power, without overlooking the intrinsic problems associated with the interpretability of the results.

Fuzzy Logic Decision Model for Robust Risk Management in ubiquitous environment- A Review

Soft computing models are invaluable resources for solving addressing real-world issues. Soft computing is the process of solving difficult computer problems with imprecise yet practical results through the application of approximate computations. The method enables the resolution of issues that might be too difficult or time-consuming to handle with the technology available today. The term ‘computational intelligence’ is often used interchangeably with soft computing. This article will discuss various soft computing strategies, with a particular focus on the fuzzy logic approach. The study delves into risk management in the real world using fuzzy logic techniques across multiple fields, providing a comprehensive review and comparing performance analyses.

A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations.

Watch your Wallet closely with online microloans: a two-stage model for delinquency and default risk management

Watch your Wallet closely with online microloans: a two-stage model for delinquency and default risk management

DEVELOPMENT OF AN ANTI-RISK METHOD OF OPTIMIZING BUSINESS PROCESSES

The introduction of digital transformation projects into the socio-economic management system requires the development of new tools and approaches to information risk management as the main lever of the project's success. In order to solve this problem, the latest scientific achievements in the direction of development and implementation of anti-risk methods for various sectors of the economy were analyzed. This analysis once again confirmed the axiom about the need to manage risks, and today, information risks, which in the era of digitalization are becoming the main problem for many organizations. The purpose of this article is to develop models and an algorithm for the anti-risk method of optimizing business processes in organizations. An anti-risk method of optimizing business processes is proposed, developed on the basis of conceptual and mathematical models of information risk management in projects of digital business transformation. The work demonstrates the concept of the method, the idea of which is the simultaneous analysis of the information risks of the organization, its projects and its environment with the application of uniform countermeasures to them, which leads to a reduction in terms and costs of project implementation. Also given is a mathematical description of the information risk management model in digital business transformation projects, as a manager's toolkit for calculating key project indicators. The proposed anti-risk method of optimizing business processes allows you to eliminate the excessive cost of operations, the time of operations, the inconsistency of actions of participants and take into account information risks. It was concluded that this anti-risk method of optimizing business processes acquires practical significance and value in the process of choosing strategies for further actions in project management.

A framework for cyber-risk insurance against ransomware: A mixed-method approach

Hackers follow a standard cyber kill chain process and install ransomware payloads using phishing emails on firms belonging to critical industry, resulting in huge losses in revenue, reputation, and customer churn. This study uses a mixed-method explanatory approach to mitigate ransomware attacks. We present the quantitative Ransomware Risk Management Model (R2M2) based on protection motivation theory (PMT). The Ransomware Risk Assessment module based on the threat appraisal component of PMT and the National Institute of Standards and Technology (NIST) guidelines can help the chief information security officer (CISO) to assess the risk using predictive analytics techniques. The Ransomware Risk Quantification module uses collective risk modeling to compute the severity of a ransomware attack on an organization. The Ransomware Risk Mitigation module helps the CISO to minimize the probability and impact of a ransomware attack on their organization by (i) investing in perimeter security technologies and training to ensure prepare, deter, detect, restrain, recover, and review mitigation strategies, (ii) adopting the National Institue of Standards and Technology (NIST) Cybersecurity Framework to ensure business resilience, and (iii) mitigating the residual risk by investing in cyber-risk insurance. We validated the proposed method using a qualitative study by interviewing participants form firms affected by ransomware, managed security service providers, security consultants, and cyber-risk insurers.

МОДЕЛІ УПРАВЛІННЯ РИЗИКАМИ З ВИКОРИСТАННЯМ СУЧАСНИХ ІНФОРМАЦІЙНИХ ТЕХНОЛОГІЙ

Risk management requires the use of a number of mathematical models for calculation. To use these models, it is necessary to form large databases on the quantitative characteristics of both the studied objects/processes/systems and the surrounding environment and conditions of the task. Implementation of this program requires the use of modern information technologies. The article analyzes the methodology for the formation of quantitative risk values, which allows for the formation of approaches to a certain standard for performing risk analysis for complex situations. A system of mathematical models and methods is proposed that allow building a system of quantitative modeling of risk situations for a wide class of objects/processes/systems. The article provides a comparative analysis of deterministic and stochastic risk management models, analyzes the riskiness model of risk stratification and the model for detecting differences from white noise. For the latter, a comparison of the experimental and theoretical concepts of the models was made. Information technologies that help organizations effectively identify, analyze and manage various types of risks, while providing a reliable protection mechanism against possible threats, such as ERM Systems, GRC Platforms, IRM Solutions, Cybersecurity Risk Management, Project Risk Management and Data Risk, are highlighted and analyzed Management.

Multi-criteria decision-making model for risk management in modular construction projects

The modular sector needs a precise guide to determine the most efficient risk management approaches. The main research objective of this study is to develop a multi-criteria decision-making model to find the most efficient risk management approach according to the relevant risk criteria. The risk criteria and risk management approaches for modular construction projects were also identified and classified within this scope. A systematic literature review, semi-structured interviews, and open-ended questionnaires were performed for identification and classification purposes. For ranking and quantifying the identified risks and risk approaches, as well as developing the decision-making model, the Delphi method and the Analytical Hierarchy Process (AHP) were conducted. A two-round Delphi method, with eleven experts, was conducted to achieve efficient performance scores of the identified risk management approaches. The percentage standard deviation decreased, Relative Importance Index (RII), Cronbach’s alpha, and Kendall’s coefficient of concordance (Kendall’s W) were calculated to ensure the outputs’ reliability, validity, and agreement level. The AHP method opted to quantify the Delphi method outputs, solve the multi-criteria decision-making process, and develop the multi-criteria decision-making model for risk management of modular construction projects. Triangulation results show that the critical risk categories are supply chain, health and safety, stakeholders, and governmental support. Lean principles such as the Last Planner System, Value Stream Mapping, Just in Time, and Kaizen are top-rated risk management approaches. This research’s novelty is identifying and analyzing crucial risk categories, providing the relevant risk management approaches ranked according to efficiency performance, and presenting a decision-making model as a guideline for risk management of modular construction projects.

A Quantitative Blockchain-Based Model for Construction Supply Chain Risk Management

Although the use of Blockchain Technology in construction industry has been limited, nowadays several cases of adoption of this technology in construction sector can been identified. Such examples consist of maintaining digital asset records, timestamps for contracts or transactions, multiple signature transactions, smart contracts, and the repository of real information. This paper proposes a methodology consisting of a Electre Tri multi-criteria analysis method where a list of indicators and a questionnaire are used to fill a model that can be applied to evaluate the suitability of blockchain technology as a tool to mitigate supply chain risks that small and medium enterprises face in the construction industry. The model has been applied to two companies operating in the construction industry. This study contributes to the existing literature by quantitatively assessing the adoption of blockchain technology on two real case studies – company Alpha and company Beta – to limit supply chain risk in the construction sector. The dimensions considered in the analysis are company data, payments, materials, supply chain structure and information and document flow. According to the findings, the model suggests that for company Alpha blockchain technology is recommended but not useful to mitigate risks and so improving supply chain performance. On the contrary, results show that for company Beta the implementation of blockchain technology is useful.

Evaluation of a conceptual model of supply chain risk management to import/export process of an automotive industry: an action research approach

Evaluation of a conceptual model of supply chain risk management to import/export process of an automotive industry: an action research approach

A comprehensive review of the Swiss cheese model in risk management

A comprehensive review of the Swiss cheese model in risk management

Mathematical Modeling of Risk Management Processes in IT Companies

Today, there is a rapid increase in the demand for information technologies. This is due to many different factors, such as the increase in the amount of information in the world, the increase in the number of investments in this area in the economy. IT products are characterized by a high degree of uniqueness on their own. The article defines the main mathematical models of the risk management process in IT companies. The classification of risks in the field of information technologies is presented, their features are described. The foundations for the construction of more functional and mathematically based models of IT risk management are defined, and the shortcomings of the described methods are also noted. The main parameters of risk in IT companies are considered and their types are determined. The basis for building more functional and mathematically justified models of IT risk management is summarized, and the shortcomings of the described methods are also noted. A conclusion was made about the need to analyze the theoretical aspects of risk management and their adaptation to modern market conditions and the practical application of theoretical postulates in the management system of a business entity.

Риск-менеджмент как фактор конкурентоспособности казахстанских вузов

В статье рассмотрена проблема развития риск-менеджмента в образовательной среде Казахстана. Обоснована необходимость образовательного контекста при рассмотрении категории риск-менеджмента, генезис которого связан с экономической сферой. Проведен анализ экспертных представлений о сути системы управления рисками, её роли в обеспечении конкурентоспособности вуза и качества образования. Дана характеристика вектора развития методологии образовательного риск-менеджмента. Предложен сравнительный контекст существующих классификаций рисков, в том числе в условиях высшей школы. Описаны функциональные границы риск-менеджеров казахстанских вузов, нуждающихся в адекватной модели управления рисками, научно-методическом её сопровождении. Проведённый анализ свидетельствует о процессе становления риск-менеджмента в системе высшего образования Казахстана и формировании необходимых правовых условий. В частности, введены в действие нормативные документы, обязывающие учебные заведения Казахстана применять принципы риск-менеджмента, учитывать их при стратегическом планировании своей деятельности и сделать частью корпоративного управления вузом. Авторами раскрыт тезис о том, что в исследовательском сообществе Казахстана наблюдается стремление осмыслить риск-менеджмент как междисциплинарную категорию, сопряженную с педагогическим менеджментом. Приведены примеры обусловленности факторов конкурентоспособности вуза системой управления рисками

An integrated interval programming and input–output knowledge model for risk and resiliency management

The COVID-19 pandemic has devastated nearly every sector of the global economy. The research seeks to examine the COVID-19 pandemic’s effects on economy and its interdependencies, specifically on mining and minerals, energy, and agriculture sectors. We propose a mathematical model by combining interval programming with input–output knowledge modeling. The input–output model illustrates how pandemic-induced perturbations in one sector spread to others. According to how susceptible they are to disturbances coming from the primary sector (e.g., mining and minerals, agriculture, or energy), a ranking of the impacted sectors can serve as an essential input for risk and resiliency management. The study also determines which sectors are anticipated to suffer significant financial losses due to the pandemic, allowing decision-makers to prioritize their investment plans in light of system-level uncertainty. We further develop four distinct scenarios and analyze the influence of the three most influential sectors on others.

Large-scale energy storage system: safety and risk assessment

The International Renewable Energy Agency predicts that with current national policies, targets and energy plans, global renewable energy shares are expected to reach 36% and 3400 GWh of stationary energy storage by 2050. However, IRENA Energy Transformation Scenario forecasts that these targets should be at 61% and 9000 GWh to achieve net zero carbon emissions by 2050 and limit the global temperature rise within the twenty-first century to under 2 °C. Despite widely known hazards and safety design of grid-scale battery energy storage systems, there is a lack of established risk management schemes and models as compared to the chemical, aviation, nuclear and the petroleum industry. Incidents of battery storage facility fires and explosions are reported every year since 2018, resulting in human injuries, and millions of US dollars in loss of asset and operation. Traditional risk assessment practices such as ETA, FTA, FMEA, HAZOP and STPA are becoming inadequate for accident prevention and mitigation of complex energy power systems. This work describes an improved risk assessment approach for analyzing safety designs in the battery energy storage system incorporated in large-scale solar to improve accident prevention and mitigation, via incorporating probabilistic event tree and systems theoretic analysis. The causal factors and mitigation measures are presented. The risk assessment framework presented is expected to benefit the Energy Commission and Sustainable Energy Development Authority, and Department of Standards in determining safety engineering guidelines and protocols for future large-scale renewable energy projects. Stakeholders and Utility companies will benefit from improved safety and reliability by avoiding high-cost asset damages and downtimes due to accident events.

Distributionally robust hierarchical multi-objective voltage risk management for unbalanced distribution systems

The large-scale integration of renewable energy generators (REGs) is key to reducing carbon emissions. However, the intermittency of REG outputs poses operational risk challenges on distribution systems (DSs). This paper proposes a novel Wasserstein-based distributionally robust assessment for the voltage risk index that represents both likelihood and non-linearly increasing severity of voltage violations. We propose a hierarchical DRO model for risk management that integrates a multi-objective distributionally robust optimization (DRO) at the distribution system operator (DSO) level and a basic DRO at the user level. Then, a systematic solution method is developed, where the proposed model is equivalently reformulated into tractable bilevel mixed-integer convex programs. The effectiveness, advantages, and scalability of the proposed method are validated on the IEEE 33-bus system and unbalanced IEEE-123-bus system.

The Impact of COVID-19 Pandemic on The Construction Projects in Indonesia: Identifying the Risks from the Stakeholders’ Perspective

Covid-19, which is declared as a pandemic by the World Health Organisation (WHO) on March 11 2020 has significant impacts in many sectors around the world. Nevertheless, there is a lack of research which analyses the impact of Covid-19 in the construction projects risks in developing countries. Given the lack of research related to the construction projects risks due to Covid-19, the overall aim of the study is to identify and classify the risk in a construction project as a result of Covid-19 Pandemic in Indonesia. The methodology which is used in this study is by implementing the questionnaire approach with the targets from the related construction industry stakeholders. The findings identified five risks which have the highest possibility to occur in a construction project as a result of Covid-19, in which the highest risk is delayed funding on construction project design (Score: 0,2523) while the lowest risk is unknown threat or vandalism from the neighborhood gangster (score: 0,0136). Further study will focus on the way in which we could develop a new framework model for risk management and preventive measures of these risks adjusted to Covid-19 impact.

Governing Emerging Solar Geoengineering: A Role for Risk-Risk Evaluation?

Abstract: In the face of rapid global heating, ideas of solar geoengineering are receiving increased attention. Yet the governance of such risky emerging technologies is poorly explored. Here I examine how the risks involved in solar geoengineering might be assessed in the context of climate risks in two different models of risk management: the technocratic, and the securitized. I show that neither model alone provides a sufficient foundation for a meaningful ‘risk-risk’ assessment, and suggest a need for better defined, yet broad scope, symmetric assessments using worst-case as well as idealised scenarios, taking into account risks in research and development as well as deployment, and considering the social distribution of risks. I conclude that effective anticipatory and ethical risk assessment may usefully supplement, but cannot replace democratic political judgements on responses to climate change.

An information model for highway operational risk management based on the IFC-Brick schema

An information model for highway operational risk management based on the IFC-Brick schema

Accelerating Sustainable and Economic Development via Scientific Project Risk Management Model of Industrial Facilities

This study presents a systemic and causal model of integrated stakeholder risk management of industrial facilities under sustainable development conditions. This model allows us to analyze the main factors of stakeholder influence, namely personnel risks, conflicts, and behavioral economic factors on a scientific project. This method is based on the identification of stakeholders and determining the possibility of the presence in their activities or inaction of personnel risks, conflicts, and behavioral economic factors that can affect the success of the production of industrial facilities, as well as on the calculation of toxicity indicators for each stakeholder. This study presents information technologies for the integrated management of industrial facilities in the context of sustainable development and transition to a circular economy, which, under conditions of uncertainty, allow the manager of an industrial enterprise and his team to implement the methodology of integrated management of industrial facilities in the context of sustainable development and transition to a circular economy to ensure the successful and timely implementation of these projects to meet the needs of stakeholders.