Operational Risk Research Articles

Development of a Machine stereo vision-based autonomous navigation system for orchard speed sprayers

In orchards, radio frequency scintillation caused by high vegetation density can hinder the effectiveness of machinery auto guidance based on the Global Navigation Satellite System (GNSS). Signal interference not only leads to poor quality of machine operation but can also pose operational risks. In this work, we propose an alternative trajectory positioning system to supplement traditional GNSS-based navigation for machinery used in orchards. The aim of this research was to develop a deep learning machine stereo vision guidance system onboard an orchard speed sprayer. The developed system combines a collision avoidance methodology along with deep learning-driven machine vision for interrow positioning and a dead reckoning set of rules for alternating U-turns. The developed methodology was tested in 4 rows of an artificial orchard. The results show that it is possible for the embedded EfficientDet target detection algorithm to guide the equipment at 1, 1.5 and 2 km × h−1 with minimum average root-mean-square errors (RMSEs) of 0.24 m, 0.20 m, and 0.31 m, respectively. When the system navigation was performed using YOLOv7, the minimum average RMSEs for each row were 0.40 m, 0.48 m, and 0.43 m, respectively, for the abovementioned speeds. The U-turn by dead reckoning showed minimum average RMSE values of 0.56 m, 0.22 m, and 0.35 m for row navigation based on EfficientDet at 1, 1.5 and 2 km × h−1, respectively. For YOLOv7-based navigation in rows, the minimum average RMSE values at these speeds were 0.35 m, 0.81 m, and 0.44 m, respectively. This study contributes to the field by proposing an alternative navigation system for orchard machines that operates without the limitations associated with GNSS. In addition, our proposed guidance methodology introduces an RGB-D collision avoidance system with a demonstrated safety capacity for navigation under real scenario conditions.

IoT Integration of Failsafe Smart Building Management System

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMSs) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMSs often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMSs can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and a real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMSs significantly boosts energy efficiency and safety in smart buildings. Simulation results reveal an hourly average power savings of 36.8 kw with the integrated failsafe model for all scenarios. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.

Reliable ships: A fuzzy FMEA based risk analysis on four-ram type hydraulic steering system

Hydraulic steering systems are vital for safe and efficient navigation in maritime vessels. Failures in these systems can pose serious operational and safety risks. This study uses fuzzy Failure Modes and Effects Analysis to assess risks in a four-ram hydraulic steering system commonly used in ships. A total of 47 failure modes of the hydraulic system were identified and analyzed using domain expert input to improve the accuracy of the risk assessment. Fuzzy input and output variables are developed, followed by the construction of membership functions, a rule base, and an inference engine to compute the Fuzzy Risk Priority Numbers (FRPN). Based on the calculated FRPN values, all failure modes are ranked, with the top ten highlighted and detailed in the Discussion chapter, which concludes with recommendations for control and prevention. It was found that the most critical failure mode is “insufficient system pressure” due to defective hoses, pipes, and connectors (FRPN 6.59). The results of this study provide practical benefits to the maritime industry by enabling more precise risk identification and mitigation strategies. This approach facilitates effective maintenance scheduling, enhances system reliability, reduces operational downtime and maintenance costs, and ultimately improves the safety and efficiency of ship navigation systems.

Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model

Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model.

Sustainable Financial Strategies: Analyzing the Role of ESG in Corporate Financial Performance and Risk Management

The growing emphasis on sustainability in the corporate sector has elevated the importance of Environmental, Social, and Governance (ESG) factors in shaping financial strategies. This study investigates the impact of ESG integration on corporate financial performance and risk management. By analyzing a comprehensive dataset of firms across various industries, the research explores the relationship between ESG practices and key financial metrics, including profitability, market valuation, and risk exposure. The findings reveal that companies with robust ESG strategies exhibit improved financial resilience, enhanced investor confidence, and reduced operational risks. Additionally, the study identifies sector-specific variations in ESG's effectiveness, emphasizing the need for tailored approaches to sustainable financial strategies. This research contributes to the discourse on sustainable finance, offering actionable insights for corporate leaders, investors, and policymakers aiming to align financial objectives with long-term sustainability goals.

Value Research of HYBE Entertainment

Abstract: With the development of economic globalization, Korean entertainment companies occupy an increasingly important position in global entertainment. Under the fierce competition among Korean entertainment companies, HYBE has created several standouts K-pop groups in the past few years, thereby breaking the pattern of the three major entertainment companies, YG, SM, and JYP. HYBE has attracted a predominantly youthful crowd mainly with its unique music aesthetic, versatile group styles, and frequent returns, and has made efforts to expand its presence from the Korean domestic market into the U.S. market, where it has become a major player. It is also trying to expand its sales from the Korean domestic entertainment market to overseas markets, mainly the United States. Despite the controversy surrounding some of HYBE's groups, which has raised the company's operational risk, the company's groups have gradually gained a significant position in global entertainment. This article analyzes the value of HYBE from the perspectives of profitability and business analysis, describes the current state of the company's development, and explores the reasons for its steady growth.

Evaluating the Impact of Technological Innovations on Operational Risk Management in Financial Institutions

This study evaluates the impact of technological innovations on operational risk management in financial institutions, focusing on how emerging technologies influence risk identification, mitigation, and management processes. Employing a qualitative approach through literature review and library research, this study synthesizes recent findings on the integration of technologies such as artificial intelligence (AI), blockchain, big data analytics, and cloud computing within financial risk frameworks. Findings indicate that these technologies offer substantial improvements in risk management by enhancing accuracy in risk detection, increasing response speed, and reducing human error. AI and machine learning models, for instance, are shown to enable real-time monitoring and predictive analytics, allowing institutions to identify potential risks before they materialize. Blockchain technology improves transparency and security in transaction processing, thereby mitigating fraud-related risks. Big data analytics provides insights into customer behavior, which can help detect anomalous activities and improve decision-making. However, the study also highlights challenges, including the need for robust cybersecurity measures, potential regulatory gaps, and skill shortages in handling complex technological systems. This study provides insights for financial institutions aiming to balance technological integration with effective risk management, underscoring the need for comprehensive frameworks that align technological advancements with regulatory compliance and organizational capability. Future research should focus on empirical studies assessing the long-term implications of these technologies on risk management efficacy in diverse financial settings.

Development of an Integrated Surveillance System to Improve Preparedness for Arbovirus Outbreaks in a Dengue Endemic Setting: Descriptive Study.

Dengue fever, transmitted by Aedes aegypti and Aedes albopictus mosquitoes, poses a significant public health challenge in tropical and subtropical regions. Dengue surveillance involves monitoring the incidence, distribution, and trends of infections through systematic data collection, analysis, interpretation, and dissemination. It supports public health decision-making, guiding interventions like vector control, vaccination campaigns, and public education. Herein, we report the development of a surveillance system already in use to support public health managers against dengue transmission in Foz do Iguaçu, a dengue-endemic Brazilian city located in the Triple Border with Argentina and Paraguay. We present data encompassing the fieldwork organization of more than 100 health agents; epidemiological and entomological data were gathered from November 2022 to April 2024, totalizing 18 months of data collection. By registering health agents, we were able to provide support for those facing issues to fill their daily milestone of inspecting 16 traps per working day. We filtered dengue transmission in the city by patient age, gender, and reporting units, as well as according to dengue virus serotype. The entomological indices presented a strong seasonal pattern, as expected. Several longtime established routines in Foz do Iguaçu have been directly impacted by the adoption of Vigilância Integrada com Tecnologia (VITEC). The implementation of VITEC has enabled more efficient and accurate diagnostics of local transmission risk, leading to a better understanding of operational activity patterns and risks. Lately, local public health managers can easily identify hot spots of dengue transmission and optimize interventions toward those highly sensitive areas.

Deep learning-based multilabel compound-fault diagnosis in centrifugal pumps

In this study, the issue of centrifugal pumps used in marine engineering is addressed, as they are susceptible to malfunction owing to long-term operation in highly corrosive seawater and extreme weather conditions, resulting in operational interruptions and safety risks. We propose a high-precision intelligent fault-diagnosis method for multiple fault types based on deep learning. In this method, continuous wavelet transform is firstly employed to extract signal time–frequency domain features. Subsequently, the Swin transformer model is used to process the wavelet time–frequency images converted from signals. Finally, multilabel classification methods are combined to diagnose various complex faults. The effectiveness of the proposed method is validated using a dataset obtained from simulation experiments pertaining to centrifugal-pump faults. The results show that the proposed method achieves 100% accuracy in diagnosing 27 types of faults and provides excellent diagnosis even under limited compound-fault samples, thus offering an efficient and practical method for fault diagnosis in centrifugal pumps used in marine engineering.

FEATURES OF OPERATIONAL RISK MANAGEMENT IN THE REPUBLIC OF ARMENIA

Any type of business activity is not free from the formation of risks. Banking is also no exception. For banking business, it is important not to avoid risks in general, but to anticipate the consequences of risks and minimize their consequences. This article presents the main mechanisms for containing operational risks of banks, taking into account possible scenarios and changes. The goal is to find out how risks are managed and the possible limits of risk indicators. Forecasts of key operational risk indicators are based on banking experience and historical data, which makes it possible to monitor potential risk thresholds. According to the author, with precise management, the latter can help differentiate and limit potential risks.

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.

Effective Internal Control Towards Business Sustainability of a Selected Company in Shenzhen, China

In the vibrant and innovative landscape of Shenzhen, China, a certain company has achieved long-term sustainable business development through a series of efficient and rigorous internal control mechanisms, thereby setting a benchmark within the industry. As a pioneer of China's reform and opening-up, Shenzhen's business environment is marked by intense competition and rapid change, which imposes high demands on a company's adaptability and flexibility. The internal controls of this company not only address financial and operational risk management but also delve into the shaping of corporate culture and the fulfillment of social responsibilities. By constructing a comprehensive risk management system, optimizing internal processes, and enhancing information communication, the company has not only weathered market uncertainties but also progressively established a solid brand trust and market competitiveness. The effective implementation of internal controls has enabled the company to pursue economic benefits while achieving harmonious development in environmental, social, and governance (ESG) aspects, forming a unique sustainable business model. This model not only helps the company maintain a leading position in the rapidly changing market but also provides valuable experience and reference for other enterprises.

Risk Management As A Determinant Of Indonesian Banking Financial Performance: A Systematic Literature Approach

This study aims to understand how risk management affects financial performance in the Indonesian banking sector. In a business environment full of uncertainty, especially in the banking sector, risk management is key to ensuring financial stability. Systematic Literature This review (SLR) analyzes the findings of articles published between 2016 and 2022, focusing on the main risks faced by banks: credit risk, market risk, liquidity risk, and operational risk. The selected articles were obtained from leading journal sources in the fields of economics and business. The literature selection process includes the stages of identification, screening, and thematic analysis to identify trends and correlations between risk management and financial performance indicators such as Return on Assets (ROA) and Return on Equity (ROE). The results of the study indicate that credit risk and operational risk have the most significant impact on financial performance, while market and liquidity risks show varying effects depending on the market situation and internal conditions of the bank. These findings underline the importance of a proactive approach to risk management, including the implementation of integrated risk mitigation policies and the use of effective internal controls. Thus, risk management not only serves as a safeguard against potential losses but also as a support for business sustainability. This study recommends further research to explore new risks such as cyber threats and regulatory changes, which are increasingly relevant in the context of the modern banking industry. These results are expected to provide insights for financial practitioners and banking leaders in improving the resilience and financial performance of their companies.

Leveraging artificial intelligence in Regulatory Technology (RegTech) for financial compliance

Abstract. The rapid development of Regulatory Technology (RegTech) has introduced new methods to handle and simplify the compliance and regulatory issues surrounding financial services and products. Combining artificial intelligence (AI) with this trend is an effective way of strengthening financial services, making them safer and more convenient. The current AI technologies incorporated within RegTech applications improve the efficiency of compliance with rules and regulations, and contribute to the development and implementation of smart technology, including automated regulatory reporting, proven and trustworthy KYC processes, and reduced compliance costs. This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions. We employ empirical economic data, evidenced by realistic case studies, to demonstrate the benefits brought on by intelligent technology to financial institutions, including quicker compliance with regulatory regimes, decreased operational risk, and increased transparency and accountability. Through analysing figures offered in various cases, we show the transformed compliance landscape created by RegTech, how it becomes a solution for the diverse challenges affecting financial sectors on a global scale, and how is it going to impact the whole field of financial services in the future..

Modeling EV dynamic wireless charging loads and constructing risk constrained operating strategy for associated distribution systems

Dynamic wireless charging (DWC) is an emerging technology that enables the charging of electric vehicles (EVs) while they are in motion. However, previous load modeling methods have not thoroughly explored the detailed analysis of DWC load characteristics. Existing research only considers the single-node supply mode for dynamic wireless charging roads (DWCRs), and the assessment of operational risks arising from the uncertain DWC loads has not been addressed. This paper begins by conducting an equivalent circuit analysis of a typical EV DWC system with multiple segmented coils. We present a more accurate trapezoidal power model for a single EV. Subsequently, we model the aggregated EV DWC load, accounting for traffic flow and headway using Poisson and negative exponential distribution functions, respectively. In the operation process, we consider a multi-node supply mode for DWCRs. To address the inaccuracy of long-term predictions, we propose a rolling optimization model to coordinate DWC and renewables with heterogeneous uncertainties by introducing a risk metric to manage potential uncertain risks. The proposed optimization model is transformed into a mixed-integer second-order cone programming (MISOCP) problem after convex relaxation. Finally, we conduct case studies to validate the proposed methods.

Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk

Operational risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures, or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technical failures and failures in the execution and administration of processes. However, the low frequency with which a loss event occurs creates a technological challenge for insurers in estimating the operational value at risk (OpVar) for the protection derived from an organization’s business activities. Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). For the estimation of OpVar, the DFCS model incorporates a novel activation function based on the generalized log-logistic function to model random variables of frequency and severity that define a loss event (linguistic random variables), as well as a credibility surface to integrate the magnitude and heterogeneity of losses in a single structure as a result of the integration of databases. The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. These features make the DFCS model a reference for estimating the OpVar to protect the risk arising from an organization’s business operations by integrating internal and external loss event databases.

How Pilot Fatigue Affects Operational Flight Risk in Scheduled Commercial Aviation

Abstract: Pilot fatigue remains a hazard in scheduled commercial aviation, contributing to 31 aircraft accidents between 2005 and 2022, according to International Air Transport Association (IATA) (2023) . Therefore, this study further investigated fatigue and its impact on operational flight risk in commercial aviation. More than 16,000 safety reports were analyzed using mixed methods. The results showed that fatigue-related occurrences were more likely to be reported confidentially. Factors contributing to an increased risk of fatigue included pilots’ personal factors, inadequate rest periods, or poor hotel environments. Fatigue was observed in 6% of all safety-related occurrences analyzed, but these events consumed 9% of the airlines’ risk budget. Further empirical research is required focusing on the influence of pilots’ personal factors on fatigue risk.

Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality

Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.

ANÁLISE HAZOP DE UMA UNIDADE DE DESTILAÇÃO ATMOSFÉRICA DE PETRÓLEO E SIMILARIDADE EM PROCESSOS DE PRODUÇÃO DE COMBUSTÍVEIS SUSTENTÁVEIS PARA AVIAÇÃO (SAFs)

The production of petroleum products stands out worldwide, among the production processes of non-renewable energies, as the best known worldwide. In this work, an atmospheric distillation column that processed 100,000 barrels/day (662.5 m3/h) of crude oil was simulated using the CHEMCAD® software. From this procedure, the Process Flow Diagram (PFD) and the Piping and Instrumentation Diagram (PI&D) were obtained for the unit, corresponding to its automation. Subsequently, a HAZOP (Hazard and Operability Study) analysis was conducted in order to identify risks in the unit that, although not dangerous, could compromise its ability to achieve its productivity. The main risks of the process were related to fires, explosions and environmental contamination from leaks and ruptures in pipes, pumps, heat exchangers, the column itself, among other equipment. The HAZOP analysis was able, through the use of the guide words, to identify the possible operational risks arising from deviations in the operating intentions, such as the possibility of fires, explosions, environmental contamination and its consequences. It was possible to identify the risks that are associated with the selection of materials, the mechanical design of the equipment and the specifications of the accessories. Therefore, the study carried out is an important survey to reduce possible failures in the oil industry. And, this study being a deepening of the distillation processes for petroleum products. It is understood that its deepening, as a result of the development of new renewable products, especially sustainable aviation fuels (SAFs), due to being similar purification processes, employ the same methodology to identify possible failures in production.

A resilience-driven emergency maintenance operation scheme optimization method based on risk

System failure caused by unexpected disruptions is unavoidable and can lead to catastrophic consequences. To complete the emergency maintenance operation safely and efficiently, a well-formulated emergency maintenance scheme is crucial. A resilience-driven optimization methodology for the emergency maintenance operation scheme with considering maintenance operational risk is proposed. The maintenance operational risk is assessed by combining Job Safety Analysis, Bayesian networks and the matter element theory. A graphical evaluation and review technique (GERT) model is established based on the informed maintenance operational risk to evaluate the emergency maintenance scheme. According to derived results of the GERT model, the expected time and the expected risk are preliminarily optimized. A re-optimization model focusing on the system resilience is established to optimize the suitable maintenance operation time for each equipment and the reasonable equipment maintenance sequence for the system. The emergency maintenance scheme optimization for the subsea production system is utilized to demonstrate the proposed method.