Over the past few years, the banking sector has undergone a rapid digital transformation. The combination of AI and ML is disrupting the old age systems and rule-based systems like anything. Various sectors are benefiting from implementing Artificial Intelligence (AI) and Machine Learning (ML) technologies to automate, personalise, and predict further changes in the customary banking model. In this paper, the study analyses the role of Machine Learning in improving customer experience and managing risk in the banking industry. Additionally, this paper discusses recent banking use-cases for chatbots, credit scoring systems, fraud detection systems, and anti-money laundering systems built using AI and ML. It also covers the ethical and regulatory aspects of AI and ML in the banking industry, including the concerns of privacy, algorithmic accountability, and algorithmic transparency. The report ends with a brief description of new technologies such as Explainable AI, Quantum Computing, and the use of Blockchain technology in financial systems. With the help of Artificial Intelligence and ML, customer experience has continued to improve, and risk management in the financial system is getting better. This study adopts a conceptual and literature-based analytical approach, and also makes an attempt to draw insights from recent empirical studies and industry reports are used to synthesise the applications of AI and ML in modern banking. The paper contributes by integrating dual perspectives, viz., customer experience and risk management, into a unified analytical framework. This study provides a structured understanding of how AI and ML combinedly enhance banking operations, offering theoretical and managerial insights for future empirical research.
Heba A. Nasr, Nasr-Eldin M. Aref, Mahmoud R. Abd Ellah
et al.
Abstract Background In the livestock industry, Foreign Body Syndrome is a devastating disease condition. Feeding management, lacking of food discrimination, and eating chopped food increase the risk of swallowing sharp foreign bodies in bovine species. In addition to the honeycomb cells shape of the reticulum, the contractions of the reticular wall, gravid uterine pressure, and parturition efforts, foreign bodies can penetrate the reticular wall, causing cascade of problems including traumatic reticulitis, traumatic reticuloperitonitis, and traumatic pericarditis. The present study was carried out to evaluate the diagnostic significance of cardiac troponin I rapid test cassette and other cardiac biomarkers including serum cardiac troponin I (cTn I), creatine kinase-myocardial band (CK-MB), lactate dehydrogenase (LDH), and aspartate aminotransferase enzyme (AST), in confirmed cases of traumatic pericarditis (TP) and/or traumatic reticuleoperitonitis (TRP) in cattle and buffaloes. Methods A total number of 30 animals (22 cattle and 8 buffaloes) with different signs such as anorexia, jugular distension, brisket edema, and signs of pain (reluctance to move, arching back, and abduction of the forelimbs) were included in the present study. Based on case history, clinical signs, ferroscopic, pericardiocentesis, radiographic and ultrasonographic examinations, TP were confirmed in cattle (n = 10) and buffaloes (n = 8) while TRP were confirmed only in cattle (n = 12). Additionally, 20 clinically healthy animals (n = 10 cattle and 10 buffaloes) were used as a control group. Blood samples were collected for determination of blood level of Tn-I, and activity of CK-MB, LDH, and AST. Results The obtained results revealed a highly significant increase in serum cTn I in diseased cattle with TP and TRP (P = 0.00), while buffaloes with TP showed no significant changes in serum cTn I (P = 0.111). Both diseased cattle and buffaloes showed increased serum activities of CK-MB, AST, and LDH enzyme. On the other hand, cardiac troponin I rapid test cassette failed to detect cTn I in diseased animals. Conclusion The study concluded that the cardiac troponin I rapid test cassette did not have a diagnostic significance and could not be used as a point-of-care under field condition for diagnosis of TP and TRP in large ruminants. However, the serum troponin I level is helpful in diagnosis of TP and TRP in cattle. Although cardiac biomarkers have some diagnostic values in TP and TRP, the traditional diagnostic methods (clinical, radiography and ultrasonography examinations) are crucial for thorough evaluation of TP/TRP cases in bovine.
Onum Friday Okoh, Emmanuel Adikwu Ukpoju, Adah Otakwu
et al.
Construction management is a complex and multifaceted discipline that plays a pivotal role in the successful execution of construction projects; despite advancements in technology and project management methodologies, construction projects continue to face a range of common issues that can significantly impact timelines, budgets, and overall project outcomes; this paper provides an in-depth review of the most prevalent challenges encountered in construction management, with a focus on identifying the root causes and proposing practical solutions; key issues explored in this review include delays in project timelines, cost overruns, quality control deficiencies, labor shortages, and communication breakdowns among stakeholders; delays, often resulting from unforeseen site conditions, regulatory approvals, and supply chain disruptions, are analyzed in detail, highlighting their cascading effects on project costs and schedules; the paper also examines the persistent problem of cost overruns, delving into factors such as inaccurate initial estimates, scope changes, and the underestimation of risks; quality control is another critical issue discussed, with a focus on how lapses in adherence to standards and specifications can lead to costly rework and long-term performance issues; the review also addresses the challenge of labor shortages, which have become increasingly prevalent due to demographic shifts and changing industry dynamics, impacting productivity and project delivery; furthermore, the paper explores how communication breakdowns between stakeholders, including contractors, subcontractors, and clients, can lead to misunderstandings, errors, and delays, ultimately affecting the project’s success; by synthesizing insights from a broad range of case studies and industry reports, this paper not only identifies the most common issues in construction management but also offers evidence-based strategies to mitigate these challenges; emphasizing the importance of proactive planning, robust risk management, and effective communication, this review aims to provide construction professionals with the knowledge and tools needed to navigate the complexities of modern construction projects successfully; the findings underscore the critical role of continuous improvement in construction management practices to enhance project outcomes and ensure the long-term viability of the construction industry.
Abstract: The current study used Blomquist’s Project Management Self-Efficacy assessment to collect data about student’s perceived project management competence before taking a graduate level project management course. The research utilized the PMSE assessment to measure actual and retrospective pre-course project management self-efficacy. No industry is immune to the need for skilled project managers. Our question is “how do you know…they know…what they say they know?” In recent years, PMI’s focus was The Project Economy. In the new project economy individuals need to transform ideas into reality and deliver value to stakeholders by collaborating in teams to successfully complete projects and support organizational value streams. Findings showed that individuals with previous project management experience were more likely to overestimate their skillset before taking the class than those without previous project management experience. This gap in project management skill insight puts employers at risk for missed opportunities and unrealized cost savings. Through the application of project management tools and templates, the students learn project life-cycle approaches used in industry today and demonstrate application through completion of actual projects in a team-based setting. The real-world application of ideas allows students to bridge the gap between their conceptual knowledge and their ability to effectively manage a project.
When predicting the defaults of a large number of samples in a region, this will be affected by industry default heterogeneity. To build a credit risk model that is more suitable for Chinese-listed firms, which have highly industry-specific default heterogeneity, we extend the forward intensity model to predict the defaults of Chinese-listed firms with information about the default heterogeneity of industries. Compared with the original model, we combine the Bayes approach with the forward intensity model to generate time-varying industry-specific default heterogeneity indicators. Our model can capture co-movements of different industries that cannot be observed based on the original forward intensity model so that the model can flexibly adjust the firm’s PD according to the industry. In addition, we also consider the impact of default heterogeneity in other industries by studying the influence of the level and trends of other industries’ default heterogeneity on a firm’s credit risk. Finally, we compute PDs for 4476 firms from January 2001 to December 2019 for 36 prediction horizons. The extended model improves the prediction accuracy ratios both for the in-sample and out-of-sample firm’s PDs for all 36 horizons. Almost all the accuracy ratios of the prediction horizons’ PDs are increased by more than 6%. In addition, our model also reduces the gap between the aggregated PDs and the realized number of defaults. Our industry-specific default heterogeneity indicator is helpful to improve the model’s performance, especially for predicting defaults in a large portfolio, which is of significance for credit risk management in China and other regions.
Gebremeskel Mamu Werid, Farhid Hemmatzadeh, Darren Miller
et al.
Infectious diseases of cattle, including bovine viral diarrhea (BVD), pose a significant health threat to the global livestock industry. This study aimed to investigate the prevalence and risk factors associated with bovine viral diarrhea virus (BVDV) infections in cattle populations through a systematic review and meta-analysis. PubMed, Web of Science, and Scopus were systematically searched for relevant articles reporting the prevalence of and associated risk factors in studies published between 1 January 2000 and 3 February 2023. From a total of 5111 studies screened, 318 studies were included in the final analysis. BVDV prevalence in cattle populations was estimated using various detection methods. The analysis detected heterogeneity in prevalence, attributed to detection techniques and associated risk factors. Antibody detection methods exhibited a higher prevalence of 0.43, reflecting the cumulative effect of detecting both active and past infections. Antigen detection methods showed a prevalence of 0.05, which was lower than antibody methods. A prevalence of 0.08 was observed using nucleic acid detection methods. The health status of the examined cattle significantly influenced the prevalence of BVDV. Cattle with bovine respiratory disease complex (BRDC) exhibited higher antibody (prevalence of 0.67) and antigen (prevalence 0.23) levels compared to cattle with reproductive problems (prevalence 0.13) or diarrhea (prevalence 0.01). Nucleic acid detection methods demonstrated consistent rates across different health conditions. Age of cattle influenced prevalence, with higher rates in adults compared to calves. Risk factors related to breeding and reproduction, such as natural or extensive breeding and a history of abortion, were associated with increased prevalence. Coinfections with pathogens like bovine herpesvirus-1 or <i>Neospora caninum</i> were linked to higher BVDV prevalence. Management practices, such as commingling, introducing new cattle, and direct contact with neighboring farms, also influenced prevalence. Herd attributes, including larger herd size, and the presence of persistently infected cattle, were associated with higher prevalence. These findings indicated the importance of detection methods and risk factors in BVDV epidemiological studies.
One of the main sectors that makes heavy use of the development of advanced computational methods is the banking sector. The goals of our research are as follows: 1) to compare scientific and regulatory approaches to defining artificial intelligence (AI) and machine learning (ML), 2) to propose AI and ML definitions for regulatory purposes that allow us to clearly state if a given method is AI/ML or not, 3) to compare the complex quantitative methods applied in banking in terms of complexity and interpretability in order to provide a clear classification of methods to the interested parties (practitioners and management), 4) to propose a possible approach towards the further development of quantitative methods in the areas of required strict interpretability. Our literature review focuses on the definitions of AI/ML applied by scientists and regulators, as well as the proposals of application of complex quantitative solutions in different banking domains. We propose practical definitions of AI and ML based on the current state of the art and requirements of clarity in the banking industry (a very limited risk appetite regarding regulations non‑compliance) and compare quantitative methods applied in different banking domains. For regulatory purposes, we propose general and inclusive definitions of AI and ML which allow for a clear classification of specific methods. In the case of strict requirements towards the interpretability of applied methods, we propose a gradual and controlled increase in the complexity of existing solutions. Therefore, we propose the differentiation of quantitative methods in terms of interpretability and complexity. We also think that the definitions of AI/ML in further regulations should make it possible to clearly say whether particular approaches are AI/ML. Our research is directed to policymakers, practitioners, and executives related to the banking sector.
The product family development process is a response to the diversified needs of markets and different customer clusters by a group of products with two fixed section part and variable section. The process is always faced with risks, usually not knowing the causative risks and the relationships between them. Also, the independent risk assumption leads to effectiveness decrease and deviations from achieving the expected results. Pay attention to the experiences of experts in mitigating risks, will lead to success. Along determining the dimensions of external and internal context, risks of the product family development process are identified by focusing on the grounded theory based on the experiences of 18 experts in Iranian automotive industry. A structural equation modeling approach was used to determining the interaction of risks and recognizing the risks that cause other risks. The results of studies and calculations led to recognition of needs risk, requirement risk, performance risk, design risk, scheduling risk, manufacturing/outsourcing risk and financial risk, also clustering of customers risk, parts design feature technical risk and modularity risk are firstly specified to the product family development process. Mitigate of risks in the early stages, leads to less occurrence probability of late-stage risks. As example to reduce financial risk, the risks that affect it, namely scheduling risk and manufacturing / outsourcing risk, must be managed. The executive managers could achieve greater success by focusing on controlling the root cause of objective risk, and make progress with more certainty.
M. Rafael Ramírez-León, Oscar Sosa-Nishizaki, Paula Pérez-Brunius
et al.
Marine mammals are highly vulnerable to oil spills, although the effects at both individual and population levels are not fully understood. A first approximation to evaluate the possible consequences of oil spills on marine life is using ecological risk assessments, which are analytical tools used to assess the likelihood of adverse environmental effects due to exposure to stressors derived from human activities. We developed a semi-quantitative framework to evaluate the risk of oil spill exposure on marine mammals that combines the likelihood of exposure based on species-specific biological and ecological traits, and the feasibility of encounter, which considers not only the overlap between the distribution of the species and the total affected area by a spill but also considers the distribution of spilled oil within this area, thus reducing the uncertainty in the estimate. We applied our framework to assess the risk of exposure of eight cetaceans to scenarios of large heavy oil (API gravity<22) spills originating from three hypothetical deep-water wells in the western Gulf of Mexico. High habitat suitability areas obtained using the maximum entropy (MaxEnt) modeling approach were used as a proxy for the geographic regions where each species is likely to be distributed, and oil spill scenarios were generated using numerical models incorporating transport, dispersion, and oil degradation. The analysis allowed identifying those species for which there is a significant risk of exposure in each spill scenario. However, our results suggest that the risk does not appear to be high for any species under any scenario. The information generated by our risk assessment is key to developing management plans in those areas of the Gulf of Mexico where deep-water activities of the hydrocarbon industry are currently being developed or planned.
Science, General. Including nature conservation, geographical distribution
Ectoparasitic salmon lice ( Lepeophtheirus salmonis ) present a major challenge to Atlantic salmon ( Salmo salar ) aquaculture. The demand for effective louse control has produced diverse management strategies. These strategies essentially impose novel selection pressures on parasite populations, driving the evolution of resistance. Here we assess the potential for salmon lice to adapt to current prevention and control methods. Lice have evolved resistance to at least four of five chemical therapeutants, and use of these chemicals has declined significantly in recent years. The industry has shifted to alternative non-chemical approaches, yet lice may adapt to these as well. Early research suggests that phenotypic variation exists in the louse population upon which non-chemical selection pressures could act and that this variation may have a genetic basis. From the existing evidence, as well as an examination of evolutionary processes in other relevant parasite and pest systems, we conclude that the evolution of non-chemical resistance is an emergent concern that must be considered by the industry. We recommend areas for focused research to better assess this risk. It is also important to determine whether phenotypic shifts in response to non-chemical selection may shift the ecological niche of the parasite, as this may have cascading effects on wild salmon populations. We also recommend further research to identify strategy combinations that have antagonistic selective effects that slow louse evolution and those with synergistic effects that should be avoided. Greater understanding of evolutionary processes can inform aquaculture policies that counteract the rise of resistant parasite populations.
AbstractKnowledge management for construction accident cases can identify dangerous conditions and prevent accidents by controlling risks on-site. However, because accident cases are recorded as un...
Prefabricated construction is one of the solutions to the problem of balancing environmental improvements with the new buildings in the construction industry. Some work originally done on site is transferred to the front end, and the occupational health risks to industrial workers during the production of prefabricate concrete components are thus aggravated. This study aims to propose a framework to simulate the occupational health risks of workers in prefabricate concrete component plants from the perspective of risk identification, risk assessment, and risk control. Through the following 4 steps, including environmental release monitoring, diffusion and human inhalation mechanism analysis, occupational health risk evaluation, and full-path health risk simulation, this study maps physical entities to virtual reality. The proposed method tends to address the root causes behind occupational health risks, such as the lack of measurement, assessment and prevention criteria, and providing new ideas for theoretical research and innovative practice of HSE management and risk management in the construction industry.
Maciej Urbaniak, Dominik Zimon, Peter Madzik
et al.
The need to evaluate suppliers from the perspective of risk analysis by purchasing companies is increasing. Such evaluation of suppliers is conducted primarily by production companies with implemented quality (QMS), environmental (EMS), health and safety management systems (H&SMS), as well as Toyota Production System (TPS). This article aims to examine latent factors for suppliers' evaluation and to describe the intensity of these factors by the implemented management system. The article provides the results of empirical research conducted with the computer-assisted telephone interviewing (CATI) technique in 151 medium and large manufacturing companies operating in Poland. The risk was classified into three main groups to deepen the research process: management system risks, environment risks, and process risks. This allowed for the formulation of some original conclusions. The results showed that companies implementing standardized management systems take the issue of risk analysis and management more seriously than organizations that do not implement such systems. The research also highlighted the differences in the perception of risk caused by implementing various management systems. The study also found that the industry and business profile specificity also affect the risk assessment in cooperation with suppliers.
The authors compare the post-issue stock and operating performance of rights issue versus public offer firms using Korean data. The authors find that the stock returns of rights issue firms are less negative than those of public offering firms during the three years subsequent to the seasoned equity offering. The authors further find that the profitability of rights offering firms is superior to those of public offering firms and that the ratio of sales to assets for rights issue firms is much higher over the post-issue period. The results substantiate Heinkel and Schwartz’s (1986) and Eckbo and Masulis’ (1992) theoretical models that posit firms with better quality tend to select the rights issue rather than public offer method when issuing seasoned equity.