Application Of Risk Management Research Articles

Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation

In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. To address these challenges, this study defines the concept of urban flood resilience and outlines its practical applications in flood risk management, proposing an integrated resilience governance framework. The framework systematically enhances urban flood management by combining structural flood mitigation methods with advanced technologies, including the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithms (MLAs). This integrated approach aims to improve early flood warning systems, optimize urban infrastructure planning, and reduce flood-related risks. The case study of the Cypress Creek watershed validates the framework’s effectiveness under specific scenarios, achieving reductions of 25% in inundation area, 30% in peak flow, and 20% in total flood volume. These results not only demonstrate the framework’s efficacy in mitigating flood impacts but also provide empirical support for developing resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management.

Integration of PSPU-Net GI Neural Network with Gravity Data for Enhanced 3D Basement Relief Estimation

Estimating the sediment-basement interface is critical to understanding basin evolution and its applications in energy, water resources, and seismic risk management. We develop PSPU-Net GI (gravity inversion) network, a deep-learning approach combining Pyramid Scene Parsing Network and U-Net, for gravity data to recover the sediment-basement interface. Training and validation involve smoothed basement models generated from random rectangles followed by filtering. We also incorporate uplifted basements and intrusions to enhance performance in complex geological contexts. Numerical results for synthetic models demonstrate PSPU-Net GI's effective recovery of sediment-basement interface relief. To improve field data predictions, we implement transfer learning and normalization strategies. Transfer learning constructs a small number of additional basement models based on the site-specific prior information and fine-tunes the neural network trained on the original general models. Normalization strategy provides a convenient way of harnessing depth information from seismic and wells. We apply our framework to the gravity data from the western margin of the Pannonian Basin (Austria). The predictions from the three implementations mentioned above (baseline PSPU-Net GI, PSPU-Net GI + transfer learning, PSPU-Net GI + normalization) successfully characterize the basement relief, and are consistent with results in previous publications. Compared with the prediction from baseline PSPU-Net GI, the prediction accuracies obtained from PSPU-Net GI implementations with the additional transfer learning and normalization components are notably improved.

Aggregate: fast, accurate, and flexible approximation of compound probability distributions

Abstract Aggregate implements an efficient fast Fourier transform (FFT)-based algorithm to approximate compound probability distributions. Leveraging FFT-based methods offers advantages over recursion and simulation-based approaches, providing speed and accuracy to otherwise time-consuming calculations. Combining user-friendly features and an expressive domain-specific language called DecL, Aggregate enables practitioners and nonprogrammers to work with complex distributions effortlessly. The software verifies the accuracy of its FFT-based numerical approximations by comparing their first three moments to those calculated analytically from the specified frequency and severity. This moment-based validation, combined with carefully chosen default parameters, allows users without in-depth knowledge of the underlying algorithm to be confident in the results. Aggregate supports a wide range of frequency and severity distributions, policy limits and deductibles, and reinsurance structures and has applications in pricing, reserving, risk management, teaching, and research. It is written in Python.

Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter

ABSTRACTDeveloping and employing practically useful and easy to calibrate models for prediction of exchange rates remains a challenging task, especially for highly volatile emerging market currencies. In this paper, we propose a novel approach for joint prediction of correlated exchange rates for two different currencies with respect to the same base currency. For this purpose, we reformulate a generalized version of a bivariate ARMA model into a state space model and use the Kalman filter for estimation and forecasting of the underlying exchange rates as latent variables. With extensive numerical experiments spanning 18 different exchange rates (across both emerging markets, developing and developed economies), we demonstrate that our approach consistently outperforms univariate ARMA models as well as the random walk model in short term out‐of‐sample prediction for various exchange rate pairs. Our study fills a gap in the empirical finance literature in terms of robust, explainable, accurate, and easy to calibrate models for forecasting correlated exchange rates. The proposed methodology has applications in exchange rate risk management as well as pricing of financial derivatives based on two exchange rates.

The Role of Sharia-Compliant Financial Institutions in Promoting Economic Stability: Insights from Recent Scholarly Contributions

This research examines the role of Islamic financial institutions in promoting economic stability through a literature study of recent scholarly contributions. Islamic financial institutions are seen as alternative financial instruments that operate under Islamic principles, which prohibit usury, excessive speculation, and non-transparent activities. This research aims to understand the mechanisms used by Islamic financial institutions in creating economic stability and evaluate the latest empirical evidence supporting such contributions. The research method used is a literature study with a qualitative approach, where various journals and research reports on Islamic finance, economic stability, and monetary policy are reviewed and analysed. The results show that Islamic financial institutions play an important role in economic stability through asset-based financing, portfolio diversification, as well as the application of risk management in accordance with Islamic principles. This research contributes to providing policy makers, academics, and financial practitioners with a deeper understanding of the potential of Islamic financial institutions as instruments that can strengthen economic stability.

Exploring the predictive power of artificial neural networks in linking global Islamic indices with a local Islamic index

This study aims to analyze the correlations and information flow between a local Sharia-compliant index and global Sharia-compliant indices using Artificial Neural Network (ANN) models, shedding light on the integration of international and national Sharia-compliant markets. Given recent market conditions and uncertainties at both local and global levels, the research investigates the predictive power of ANNs and examines the relationship between the Dubai Financial Market Sharia Index (DFMSI) and three well-known global Islamic indices: the Dow Jones Islamic Market World Index, the Standard and Poor’s Global Sharia Index, and the Financial Times Stock Exchange Sharia All World Index. By incorporating data from multiple global Sharia indices, the study expands data sources beyond the local market, providing a comprehensive analysis of factors influencing the DFMSI’s price. This approach reduces reliance on a single dataset and strengthens the robustness of the research findings. Additionally, the inclusion of the three global Sharia indices offers benefits such as access to global market insights, risk management, and practical applications for investors. The study demonstrates that ANNs, particularly the Nonlinear Autoregressive with External Input Neural Network (NARX), outperform classical models in accurately predicting the DFMSI. This research contributes significantly to the understanding of Sharia-compliant investments by providing a comprehensive analysis of how multiple global indices affect a local market index. It also offers valuable insights for investors and portfolio managers, suggesting that the integration of advanced machine learning models can enhance decision-making processes and improve risk management strategies.

Unveiling the Burden: A Six-Year Retrospective Analysis of Pressure Ulcer Epidemiology in a ICU.

This study describes the epidemiological changes in pressure ulcers (PUs) in a Portuguese intensive care unit (ICU) from January 2017 to June 2023, characterizes critically ill patients with PUs, identifies specific risk factors, and assesses the effectiveness of implemented preventive measures. A retrospective observational cohort study was conducted, analyzing records of ICU patients with PUs during the specified period. Data were extracted from the institution's Global Risk Management application and the ICU's electronic PU registry. The study included patients with ICU stays longer than 24 h and excluded those with ineligible clinical records or incomplete characterization data. Among 3816 evaluated patients, 257 developed a total of 345 PUs, averaging 1.4 PUs per patient. The average PU prevalence rate was 6.81%, with the highest prevalence in 2020 (11.0%) and the lowest in 2022 (3.48%). The average incidence rate was 3.76%, peaking at 5.71% in 2020 and declining to 2.54% in 2023. The sacrum and heels were the most commonly affected areas, with Category 2 PUs being the most frequent. Key intrinsic risk factors included systemic diseases and sensory deficits, with pressure identified as a significant extrinsic factor. Preventive measures focused on risk assessment and pressure control. The study reveals a PU prevalence of 6.81% and an average incidence of 3.76%, underscoring the need for enhanced preventive strategies, especially in anatomical areas like the sacrum and heels. It emphasizes the importance of personalized assessments, continuous education for nursing staff, and a multidisciplinary approach to improve patient outcomes and care quality in the ICU.

Implementation of Risk Management in Asset Management Governance Towards Optimizing Asset Utilization at Public Service Agency State Universities (Case Study of UPN Veteran Yogyakarta)

This study aims to analyze the application of risk management in asset governance at UPN Veteran Yogyakarta to achieve optimal asset utilization. As a Public Service Agency State University (PTN-BLU), UPN Veteran Yogyakarta faces various challenges in asset management, including budget constraints, lack of consistent monitoring, and the unavailability of formal risk evaluation standards. Qualitative descriptive research methods are used to gain in-depth understanding through interviews with asset managers and field observations. The results show that the implementation of risk management can have a significant impact on the efficiency of budget allocation, which includes more appropriate budget distribution based on priority needs and risks. Risk management also has the potential to extend the useful life of assets, with risk mitigation measures taken on assets that show signs of wear or early damage, thus preventing major repair costs due to serious damage. This research highlights that a technology-based monitoring system is an indispensable aspect to support the effectiveness of risk management in asset management. The implementation of structured risk assessment procedures helps in determining asset maintenance priorities, so that the assets that are most critical to the sustainability of campus operations receive greater attention. With the implementation of this comprehensive risk management, optimization of asset utilization can be achieved, which in turn supports the stability and sustainability of UPN Veteran Yogyakarta's operations as a quality higher education institution.

Analysis of the Implementation of Risk Management to Minimize the Risk of Bad Credit at the Nias Rural Development Savings and Loans Cooperative (KSP3) Gunungsitoli Branch

This study aims to determine the application of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch in minimizing the risk of bad loans. In this study, the type of research used is a descriptive type with a qualitative approach. Data collection techniques are used through observation, interviews and documentation. To obtain information, the researcher conducted direct interviews with 6 informants, namely branch heads, 2 employees and 3 members of the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch. The data analysis techniques used in qualitative research include transcripts of interview results, data reduction, data presentation and conclusion drawn. The results of the study show that the implementation of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch is carried out through a series of stages, namely risk identification, risk measurement, risk monitoring and risk control. The implementation of risk management to minimize the risk of bad loans has been effectively implemented by KSP3 Nias Gunungsitoli Branch as evidenced by the NPL (Non-Performing Loan) rate from 2020-2022 which is still below 5%.

Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach

This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model’s predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore, the research addresses concern regarding fairness and bias by showing that removing potentially discriminatory features, such as age and gender, does not significantly impact the hybrid model’s classification capabilities. This suggests that fair and unbiased credit scoring models can achieve high effectiveness levels without compromising accuracy. This paper makes significant contributions to academic research and practical applications in credit risk management by presenting a hybrid model that offers superior classification accuracy and promotes interpretability using the model agnostic SHAP framework.

Convolution Bounds on Quantile Aggregation

Advancing Risk Assessment: New Ways To Compute Quantile Aggregation This issue features a pivotal study on quantile aggregation amid dependence uncertainty, an area critical to finance, risk management, and statistics. The authors introduce “convolution bounds,” derived from a recent inf-convolution formula of quantiles and related risk measures. The obtained analytical tools unify existing results and enhance the understanding of quantile methods by providing general, sharp, and computationally efficient solutions. The results offer insights into the extremal dependence structures, with several implications in risk management and economic analysis applications. For more detailed insights, read the full paper, “Convolution Bounds on Quantile Aggregation” (reference: [insert reference]).

The Application of Fintech (Financial Technology) and Risk Management

This paper provides a comprehensive overview of the impact and applications of financial technology (Fintech) across various sectors such as payment and clearing, lending, wealth management, insurance, and risk management. It highlights the transformative potential of Fintech in enhancing financial efficiency, inclusiveness, and risk management through the utilization of modern technologies like big data, artificial intelligence, and blockchain. The paper delves into specific examples such as online payment systems, peer-to-peer lending platforms, robo-advisory services, and blockchain technology. Furthermore, it discusses the challenges and risks associated with Fintech, including regulatory issues, security concerns, and the need for effective risk management practices. The abstract emphasizes the importance of responsible risk management in ensuring financial stability and preventing systemic risks in the Fintech sector. Overall, the abstract provides a comprehensive understanding of the role of Fintech in shaping the future of the financial industry and emphasizes the need for effective regulation, risk management, and technological innovation to harness its full potential while mitigating associated risks.

Distortion Risk Measures of Increasing Rearrangement

Increasing rearrangement is a rewarding instrument in financial risk management. In practice, risks must be managed from different perspectives. A common example is the portfolio risk, which often can be seen from at least two perspectives: market value and book value. Different perspectives with different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as an increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce increasing rearrangement for application in financial risk management and to apply increasing rearrangement to the class of distortion risk measures. We derive formulae to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely the value at risk, expected shortfall, range value at risk, conditional value at risk, and Wang’s risk measure. Finally, we present the connection of increasing rearrangement with inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as an illustrative example of the method.

The Impact of Artificial Intelligence on Project Management Efficiency

This research paper examines the influence of Artificial Intelligence (AI) on the efficiency of project management, concentrating on the ways in which AI tools and techniques improve different facets of project management. The study initiates with a comprehensive examination of project management and the increasing significance of AI in various domains, emphasizing the shortcomings of conventional methodologies and the capacity of AI to tackle these issues. The study seeks to evaluate the impact of AI on project processes, pinpoint effective AI tools, and investigate the advantages and obstacles associated with AI integration. A thorough examination of existing literature investigates AI tools, including machine learning, natural language processing, and predictive analytics, along with their applications in planning, scheduling, resource allocation, risk management, communication, and performance monitoring. Case studies and empirical evidence illustrate the beneficial effects of AI on project outcomes, while also highlighting existing gaps in the literature that warrant further investigation. The mixed-methods approach integrates qualitative and quantitative data sourced from surveys, interviews, and case studies, employing both thematic and statistical analysis. The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration. Nonetheless, factors such as initial investment, skill requirements, resistance to change, and ethical considerations are acknowledged. The discussion outlines actionable strategies for project managers, highlighting the importance of training, incremental integration, ensuring data quality, and promoting innovation. The study concludes by offering recommendations for future research regarding the long-term effects of AI, conducting cross-industry analyses, and creating AI tools specifically designed for project management. This approach aims to provide empirical evidence and strategic insights into the integration of AI for enhanced project outcomes.

Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management

Strategic behavior is common in multi-criteria sorting, where manipulated alternatives are allocated to the target category to achieve the intended goal. Engaging in such strategic behavior in sorting consensus often comes at a cost, which is closely tied to the degree of preference adjustments and the level of trust relationships among decision makers. This study investigates strategic behavior using piecewise cost-based consensus mechanism in social trust network. Firstly, the trust relationship dependent piecewise cost is formally defined and formulated by considering the impact of trust relationship on the preference adjustments willingness to achieve sorting consensus. Then, based on the trust relationship dependent piecewise cost, a preference strategic manipulation model is proposed to achieve strategic behavior in sorting consensus, which is implemented by minimizing the cost of strategically guiding decision makers in adjusting their preferences. Considering that social trust network affects strategic manipulation, a preference-network collaborative strategic manipulation model is further proposed, which is designed to strategically guide decision makers in adjusting both their preferences and social trust networks. Moreover, the practicality of proposed strategic manipulation models is exemplified through a case study on supply chain risk management and their performance is validated via simulation analysis.

Effectiveness of Risk Management through Risk Maturity Measurement: A Study at PT Pelabuhan Indonesia

This research aims to evaluate the effectiveness of risk management at Pelindo post-merger and assess its risk management maturity level. Additionally, the study intends to offer relevant recommendations for the company in developing its risk management strategy and implementation. The research adopts a qualitative descriptive approach with a case study strategy, where the researcher analyzes an organization as a unit of analysis in a natural setting without manipulation. Data collection techniques include interviews with representatives from the Group Risk Management, Governance, and Compliance as the corporate-level risk managers. Document analysis includes textbooks, academic and professional journals, theses, articles, and company department reports. Data analysis follows the Miles and Huberman model. The research indicates that the maturity level of risk management at Pelindo post-merger has reached the good practice phase with a score of 2.94. This reflects a consistent application of risk management in accordance with industry standards, although its effectiveness varies. The organizational structure, framework, processes, and risk culture have begun to be implemented. Recommendations for improvement include strengthening the risk culture, enhancing the effectiveness of risk management functions, and aligning risk strategies with the ERM transformation plan for better effectiveness and compliance.

The impact of predictive analytics on financial risk management in businesses

This paper examines the impact of predictive analytics on financial risk management in businesses. Predictive analytics involves the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future outcomes. In the context of financial risk management, predictive analytics plays a critical role in identifying, assessing, and mitigating potential financial risks. This paper explores various machine learning algorithms, including neural networks, decision trees, and support vector machines, and their applications in risk management. It also discusses data sources, preprocessing techniques, and the challenges associated with data privacy, model interpretability, and prediction accuracy. The review highlights successful implementations of predictive analytics in financial risk management and provides recommendations for future research and practical applications. As predictive analytics continues to advance, its integration with emerging technologies such as artificial intelligence and blockchain promises to enhance financial risk management practices.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

Hukum Islam pada Imple¬mentasi Manajemen Risiko Penyelesaian Pembiayaan Bermasalah Bank Syariah Indonesia (BSI)

Islamic banking continues to develop, particularly in product diversification, offering a wider variety of options that have positively impacted the public's willingness to conduct muamalah activities through Islamic banks. Various financing products provide customers with the convenience of selecting options that suit their needs. The object of this research, as explained by Husein Umar in Setiawan Santana, focuses on problematic financing, a condition where significant deviations occur in repayments, leading to delays or potential losses. Problematic financing represents a situation where loan repayment agreements face the risk of failure and tend to lead to potential losses. This study offers new insights by examining the role of Islamic law in managing problematic financing risk at Bank Syariah Indonesia (BSI). The novelty of the research lies in the further exploration of using Islamic contracts (akad) as the main foundation in regulating and resolving problematic financing. Through this approach, the research provides a fresh perspective on how Islamic banks can more effectively manage financing risks while adhering to Sharia principles. The impact of this research on Islamic banking is quite significant. The application of risk management based on Islamic law is expected to enhance public trust in Islamic banking and strengthen its stability in Indonesia. The use of akad as the legal basis in resolving problematic financing ensures transparency and fairness in line with Sharia principles. This provides protection for both customers and the bank, creating a stronger foundation for the sustainability of Islamic banking in the future.

Integrating risk management in implementing circular economy principles in the healthcare sector: a case study from Indonesia

Purpose The purpose of this study is to explore the integration of risk management and circular economy (CE) principles within the healthcare sector to promote sustainability and resilience. Specifically, the study aims to demonstrate how risk management can support the transition to a circular economy in healthcare supply chains. By integrating risk management practices with CE principles, healthcare organizations can identify potential risks and opportunities associated with circular initiatives. Design/methodology/approach This study adopts a qualitative research approach, using a case study methodology with semi-structured interviews conducted at primary care facilities to understand the application of CE principles in practice. The study uses fuzzy logic methods to assess and mitigate risks associated with strategies promoting CE principles. Additionally, key performance indicators are identified to evaluate the effectiveness and enhance the resilience of these strategies within healthcare supply chains. Findings The study highlights the critical role of robust risk management strategies in facilitating the transition to a circular economy within healthcare organizations. Primary care facilities, which are critical to frontline healthcare delivery, are particularly vulnerable to product shortages due to supply risks. This study focuses on critical protective equipment, specifically latex gloves and assesses operational risks, including supply, demand and environmental risks, using a fuzzy logic-based model. Import delays were found to be a moderate risk, typically occurring once a year. The research highlights critical KPIs for a successful CE transition within healthcare supply chains, such as on-time delivery and service quality, which are directly related to the risk of supply chain disruption. In addition, the study highlights the significant impact of other CE strategies on healthcare supply chains, including localized production and manufacturing, innovation in product development, reverse logistics, closed-loop supply chains and the adoption of lean principles. Practical implications This study provides valuable insights for healthcare organizations to optimize resource efficiency, reduce waste and promote circularity in their operations. By implementing the proposed solutions and focusing on the identified KPIs, organizations can develop strategies to achieve sustainability goals and enhance resilience in healthcare supply chains. Originality/value This study contributes to the literature by demonstrating the application of risk management in facilitating the transition to a circular economy in the healthcare sector. The use of fuzzy logic methodology offers a novel approach to assessing and mitigating risks associated with critical product failures in supply chain activities. The study’s findings provide practical guidance for healthcare organizations seeking to integrate circular economy principles and improve sustainability performance.