Search Results (26)

Evidence from observational studies plays a central role in shaping public policy in health, education, and financial regulation, where randomized experiments are rarely feasible. Propensity score matching (PSM) is a widely used method to approximate fair comparisons between treatment and control groups. Incorporating machine learning into the estimation of propensity scores can strengthen prediction and enhance the credibility of findings. However, stronger predictive models create a “predictability paradox”. As predictive accuracy improves, estimated propensity scores for treated and control units become more distinct when treatment assignment is strongly predictable from observed covariates, revealing limited overlap between groups. In the limit, near-perfect prediction produces near-complete separation between groups, rendering traditional matching infeasible and confining inference to a narrow subset of units near the boundary of the propensity score distribution, a setting analogous to a regression discontinuity design (RDD). Researchers thus face perverse incentives to use weaker models for statistically significant but spurious results. These dynamics jeopardize the reliability of evidence for policy. To safeguard decision-making, we propose a simple reform: require that studies using PSM disclose model error rates, including false positive and false negative rates, along with information on overlap and effective sample size. Full article

The automobile insurance industry plays a pivotal role in the financial system, fostering economic stability through effective risk management and consumer confidence. Continuous enhancement in price optimisation not only ensures the sustainability of insurers but also fosters a more competitive, fair, and balanced market, which is vital for a country’s economic development. The objective of this research is to develop a methodology for determining the optimal price offered by insurance firms for automobile policies in an industry where a First Price Sealed Bid auction system operates. A statistical methodology is employed to ascertain the expected value and standard deviation of the policies on offer in the public domain, whereby these values are calculated using a heteroskedastic linear regression estimation methodology. Furthermore, the aforementioned expected values and standard deviation enable the calculation of the value of the cumulative distribution for an optimal price set within the public offer. This study demonstrates that identifying the optimal price that maximizes profits is analogous to establishing an expected market share for each niche automobile policy market. Moreover, the market share can be calculated through a straightforward heteroskedastic linear regression estimation for instances where market shares are below 50%. Full article

Anomaly detection plays an increasingly important role in maintaining the stability and reliability of modern distributed systems. Federated Learning (FL) is an emerging method that shows strong potential in enabling anomaly detection across decentralised environments. However, there are some crucial and tricky research challenges that remain unresolved, such as ensuring scalability, adaptability to dynamic server clusters, and the development of standardised evaluation frameworks for FL. This review aims to address the research gaps through a comprehensive analysis of existing studies. In this paper, a systematic review is conducted by covering three main aspects of the application of FL in anomaly detection: the impact of communication overhead towards scalability and real-time performance, the adaptability of FL frameworks to dynamic server clusters, and the key components required for a standardised benchmarking framework of FL-based anomaly detection. There are a total of 43 relevant articles, published between 2020 and 2025, which were selected from IEEE Xplore, Scopus, and ArXiv. The research findings highlight the potential of asynchronous updates and selective update mechanisms in improving FL’s real-time performance and scalability. This review primarily focuses on anomaly detection tasks in distributed system environments, such as network traffic analysis, IoT devices, and industrial monitoring, rather than domains like computer vision or financial fraud detection. While FL frameworks can handle dynamic client changes, the problem of data heterogeneity among the clients remains a significant obstacle that affects the model convergence speed. Moreover, the lack of a unified benchmarking framework to evaluate the performance of FL in anomaly detection poses a challenge to fair comparisons among the experimental results. Full article

Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and 2023, highlighting the integration of machine learning and optimization techniques, particularly bio-inspired metaheuristics, for feature selection in individual credit risk assessment. These nature-inspired algorithms, derived from biological and ecological processes, align with bio-inspired principles by mimicking natural intelligence to solve complex problems in high-dimensional feature spaces. Unlike prior reviews that adopt broader scopes combining corporate, sovereign, and individual contexts, this work focuses exclusively on methodological strategies for individual credit risk. It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. To strengthen transparency and comparability, this review also synthesizes classification performance metrics—such as accuracy, AUC, F1-score, and recall—reported across benchmark datasets. Although no unified experimental comparison was conducted due to heterogeneity in study protocols, this structured summary reveals consistent trends in algorithm effectiveness and evaluation practices. The review concludes with practical recommendations and outlines future research directions to improve fairness, scalability, and real-time application in credit risk modeling. Full article

Pre-taxation analysis plays a crucial role in ensuring the fairness of public revenue collection. It can also serve as a tool to reduce the risk of tax avoidance, one of the UK government’s concerns. Our report utilises pre-tax income ($PI$) and total assets ($TA$) data from 567 companies listed on the FTSE All-Share index, gathered from the Refinitiv EIKON database, covering 14 years, i.e., the period from 2009 to 2022. We also derive the $PI/TA$ ratio, and distinguish between positive and negative $PI$ cases. We test the conformity of such data to Benford’s Laws, specifically studying the first significant digit ($Fd$), the second significant digit ($Sd$), and the first and second significant digits ($FSd$). We use and justify two pertinent tests, the ${\chi }^2$ and the Mean Absolute Deviation (MAD). We find that both tests do not lead to conclusions in complete agreement with each other—in particular, the MAD test entirely rejects the Benford’s Laws conformity of the reported financial data. From the mere accounting point of view, we conclude that the findings not only cast some doubt on the reported financial data, but also suggest that many more investigations should be considered on closely related matters. On the other hand, the study of a ratio, like $PI/TA$, of variables that are (or are not) Benford’s Laws-compliant adds to the literature concerning whether such indirect variables should (or should not) be Benford’s Laws-compliant. Full article

Legislative regulations have recognized the significance of board independence in enhancing the board’s role and strengthening its autonomy, which are among the key features that mitigate conflicts of interest between management and shareholders. External auditing serves as a pivotal element of corporate governance, acting as a monitoring mechanism to reduce information asymmetry and safeguard principal interests by ensuring the accuracy and fairness of financial statements. This, in turn, reassures data users and stakeholders. The study aimed to examine the effect of board independence on enhancing external auditor independence among 72 Jordanian service companies listed on the Amman Stock Exchange from 2017 to 2021, with a study sample of 62 companies. The findings revealed a negative impact of board member independence on external auditor independence, as measured by audit firm size. However, company size positively influenced external auditor independence, while no effect was found for financial leverage or company age. The findings highlight the need for companies to strengthen internal controls and governance practices to enhance external auditor independence. Additionally, they suggest that company size plays a crucial role, while other factors like financial leverage and company age may have limited impact, indicating areas for further exploration in future research. Full article

Green innovation provides powerful incentives to achieve sustained social progress. However, the available research examines the financial drivers of green innovation, overlooking the impact of digital government development and the institutional environment. The integration of digital government construction with the institutional environment, and the coupling of the two with green innovation, will paint a picture of the future that promotes sustainable social progress and the modernization of governance. This research utilizes data from 31 provinces in China from 2018 to 2022 to study the impact of digital government construction and the institutional environment on the provincial green innovation efficiency. An empirical analysis is conducted on the basis of analyzing the spatiotemporal evolution and pattern of digital government construction, the institutional environment and the provincial green innovation efficiency. Firstly, digital government construction emphasizes data openness and sharing, and data become a key link between those inside and outside the government. The digital platform becomes an important carrier connecting the government and multiple subjects in collaborative innovation to continuously shape a new digital governance ecology. The netting of digital ecology is conducive to the institutional environment, serving to break the path dependence and create a more open, inclusive and synergistic institutional environment. Based on this, we consider that digital government construction positively affects the institutional environment, and this is verified. Secondly, a good government–market relationship, mature market development, a large market service scale, a complete property rights system and a fair legal system brought about by the improved institutional environment provide macro-external environmental support for enhanced innovation dynamics. Based on this, it is proposed that the institutional environment positively affects the provincial green innovation efficiency. Meanwhile, building on embeddedness theory, the industrial embeddedness of the institutional environment for green innovation highlights the scattered distribution of innovation components. Geographical embeddedness stresses indigenous resource distribution grounded in space vicinity and clustering. The better the institutional environment, the greater the forces of disempowerment at the industrial tier and the easier it is for resources to flow out. This may potentially have a detrimental role in improving the local green innovation efficiency. In view of this, it is proposed that the institutional environment negatively affects the provincial green innovation efficiency, and this is verified. Thirdly, digital government construction, as an important aspect of constructing a digital governance system and implementing the strategy of a strong network state, can effectively release the multiplier effect of digital technology in ecological environment governance and green innovation, continuously enhancing the provincial green innovation efficiency. In view of this, it is proposed that digital government construction positively affects the provincial green innovation efficiency, and this is verified. When the institutional environment is used as a mediating variable, digital government construction will have a certain non-linear impact in terms of provincial green innovation efficiency improvement. Building on the evidence-based analysis results, it is found that the institutional environment plays a competitive mediating role. This study integrates digital government construction, the institutional environment and the provincial green innovation efficiency under a unified analytical structure, offering theoretical inspiration and operational directions to enhance the provincial green innovation efficiency. Full article

The oil and gas industries are crucial to global economies, influencing geopolitics, driving technological advancements, employing millions, and impacting financial markets. The complexity and the volume of data generated by these industries demonstrate the need for efficient information management, where effective contract audits play a key role in ensuring market stability, transparency, fair revenue distribution, corruption mitigation, and enhancing industry integrity to attract investors. This study employs bibliometric analysis to explore the application of machine learning (ML) in detecting anomalous contracts within the oil and gas industry. This analysis identifies key research and challenges, laying the groundwork for further computational ML advancements. The PRISMA guidelines identify ML’s role from 2018 to 2023, including post-COVID-19. Principal component analysis (PCA) evaluates the bibliometric contributions of different countries and institutions. China, Indonesia, Egypt, Saudi Arabia, the University of Antwerp Operations Research Group, and the University of Pittsburgh emerge as significant contributors. These findings underscore ML’s pivotal role in fraud detection, risk mitigation, and cost savings, concluding that anomalous contract detection remains open to newer ML techniques and ongoing research. Full article

The purpose of this study is to determine the relationship between the leadership styles of leaders of financial technology (Fintech) start-ups and firm growth. A quantitative design employing a cross-sectional survey with the use of a Likert questionnaire was conducted on the leaders of top-performing Fintech firms in Zambia, as recognized by Tracxn in its May 2020 report. This study focuses on three leadership styles: transformational leadership, transactional leadership, and laissez-faire leadership. The most significant result is that transformational leadership is strongly associated with the growth of Fintech start-ups in Zambia, while transactional leadership plays a limited role. The association between laissez-faire leadership and firm growth is positive but weak. The research makes two main contributions to the literature in the field of Fintech. First, the findings can help researchers explain leadership styles that predict the growth of Fintech start-ups. Second, founders of Fintech firms will understand the most important leadership styles that can lead to the growth of start-ups. The limitations of this study relate to the sample size, the need to consider other readership styles, and the use of qualitative and longitudinal designs that would provide more insights and validation. Full article

Since the first occurrence of African swine fever (ASF) in wild boar in Germany in 2020, the disease has primarily affected the wild boar population in the eastern part of the country close to the border with Poland. Local hunters play a crucial role in implementing surveillance and control. To evaluate their perceptions of existing control measures and analyze regional differences between hunters from ASF-affected and non-affected regions, a questionnaire study was conducted among the German hunting community. Hunters from non-affected areas held a more optimistic view regarding the effectiveness of control measures compared to hunters from affected areas. However, control measures that hinder hunting were generally perceived as ineffective. Measures that collided with hunters’ understanding of fair hunting practices were regarded as controversial. Financial incentives and reducing bureaucracy were the most favored approaches to increase hunters’ participation. Moreover, the possibility of eating or selling the meat of hunted wild boar and the provision of infrastructure for implementing ASF control were considered motivating. Thus, this study highlights the importance of compensating hunters and addressing their concerns to maintain their engagement in ASF control. To enhance compliance with controversial measures, thoughtful communication and raising awareness are essential. Full article

Sensitive ecosystems play a major role in the future of the environment, economy, and society, as they affect and mitigate natural hazards, provide food, energy, and medicinal resources, and job opportunities, as well as cultural and recreational services. Meanwhile, the rapidly growing nature-based tourism sector is applying unsustainable pressures on such ecosystems, prioritizing the assessment of their sustainability, i.e., environmental, economic, and social functionality. To ensure long-term development and conservation, benefits from the natural capital must be valued and included in its management plan. The travel cost method (TCM), although heavily exploited in research, exhibits application challenges and methodological weaknesses. This paper seeks to comprehensively present the most recent applications of TCM, focusing on aquatic ecosystems that serve as tourist destinations, whereupon research gaps are identified, ultimately providing insights for future directions in the field. Quantifying the economic worth of sensitive ecosystems is a prerequisite to address issues, such as overexploitation, pollution, and climate change, so these problems can be alleviated in the long-run. In parallel, the critical long-term double effect of fair entrance fees is recognized, which not only motivate visitation by securing accessibility but also guarantee adequate financial resources to protect and maintain the ecosystems’ integrity. Full article

In recent times, the green transition, by promoting carbon neutrality, has become highly imperative to meet environmental challenges. The present literature review study seeks to explore the intersecting role of greener innovations in facilitating financial inclusion for a sustainable future. Within the global agenda is the goal of carbon neutrality, with the aim of reducing environmental impact and mitigating climate change. Aim: The present study aims to investigate the role that technological innovations play in the financial inclusion of achieving climate neutrality. Method: Through a systematic literature review, we investigate how new innovations generate new investment opportunities and promote sustainable development. However, fair, accessible, and inclusive financing is crucial. Findings: the analyzed documents in this study shows that technological innovations can play an important role in financial inclusion for carbon neutrality and provide some important policy implications. Indeed, a favorable regulatory environment could generate positive effects already in the short term, with important environmental, economic, and social repercussions. Full article

The use of blockchain technology can ensure that data remains untampered with once it is on the chain. However, it doesn’t guarantee the authenticity of data before it enters the chain. In this study, we developed a three-party dynamic evolutionary game model involving core enterprises, small and medium-sized enterprises (SMEs), and financial institutions. Our findings indicate that a blockchain supply chain (BSC) generates more economic benefits than a traditional supply chain (TSC). We then built a dynamic evolutionary game model between core enterprises and SMEs, which revealed that SMEs are influenced by core enterprises and tend to adopt the action strategies of the latter. Additionally, we developed a dynamic evolutionary game model between core enterprises and financial institutions and compared the reward and punishment mechanisms with the synergy payoff mechanism. In the reward and punishment mechanisms, the game is a zero-sum game, where one party’s gains come at the expense of the other party. This mechanism has certain limitations and must meet specific conditions to improve the willingness of enterprises to share data. On the other hand, the synergy payoff mechanism enhances the authenticity of shared data by increasing the payoff for participants. When core enterprises play games with SMEs, the probability of core enterprises uploading real data and the distribution ratio of synergy payoff show an inverted U-shape. Similarly, core enterprises and financial institutions have comparable results in allocating synergy payoff. To leverage the synergy payoff mechanism, the distribution proportion of players participating in the synergy payoff should be considered fair. Finally, we validated our findings by simulating the models. If we can use blockchain technology to enhance the mutual trust between enterprises and banks, both banks and enterprises can achieve sustainable development. Full article

Although the importance and benefits of logistics integration in omni-channel (OC) retailing have been discussed in the literature, the impacts of logistics integration from the dimension of internal and external logistics remain unknown. To fill this gap, this study aims to investigate the relationships among internal and external logistics integration capabilities, supply-chain integration (SCI), and financial performance (FP) in OC retailing based on the dynamic capability view. An empirical study is conducted based on a survey of 230 OC retailers in China’s market. Factor analysis and regression analysis are conducted to examine the hypotheses of the proposed conceptual model. The quantitative analyses show that the internal logistics integration capability is significantly related to the external logistics integration capability, and they both have positive effects on SCI, while the external logistics integration capability generates a higher impact (i.e., almost 1.5 times that of the internal logistics integration capability). The numerical results also demonstrate that the logistics integration capabilities and SCI have similar positive effects on FP (i.e., all the relevant regression coefficients show values around 0.25), and SCI plays a partial intermediary role in the relationships between logistics integration capabilities and FP. Furthermore, the quantitative evidence addresses the fact that the FP is not influenced by OC retailers’ characteristics, indicating a fair business environment in the OC retail industry. Full article

This paper analyses competitive balance in 24 top-division domestic football leagues in Europe before and after the implementation of UEFA’s Financial Fair Play (FFP) regulations. Our analysis covers 22 seasons between 2000/01 and 2021/22 and utilises indicators of overall league concentration and dominance. Seven of the 24 leagues examined have seen a statistically significant worsening of league concentration post-FFP, fourteen leagues experienced a decline in the number of top-four finishers and thirteen saw a reduction in the number of unique title winners. The weight of evidence indicates that FFP has adversely affected competitive balance in several European football leagues. Full article