A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Financial services for SME fisheries: the case study of South Africa

The primary objective of the South Africa case study has been to assess the availability of and access to financial services by SMEs in the fisheries sector. Fieldwork took place during 6-12 March 2011 in Cape Town, Port Elizabeth and Port St Francis, while desk research provided background information about the overall context of the economy and the fisheries sector in South Africa. This summary highlights key findings surrounding three areas: (1) overall context of finance for fisheries in South Africa; (2) current external and internal constraints to the development of the squid and tuna sectors; and (3) financial models for SMEs in the fisheries sector

Heterogeneous Graph Neural Networks for Fraud Detection and Explanation in Supply Chain Finance

It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in anongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework’s effectiveness in fraud detection and explanation across domains

Opportunities for greater Lincolnshire's supply chains: full report

A study of the key sector supply chains across Greater Lincolnshire, and identification of barriers and opportuniteis for growth

New horizons for productive transformation in the Andina Region

En cubierta: Growth and productive transition agendaBibliografía: p. 118-129Las economías desarrolladas y emergentes, así como el contexto internacional dentro del cual interactúan, han enfrentado eventos que vienen transformando estructuralmente sus procesos de producción. El cambio climático, la digitalización y la pandemia del COVID-19 están generando grandes cambios en el mundo. La estructura productiva de los países andinos está siendo afectada por estas tendencias. Dada esta coyuntura, urge tomar decisiones sobre políticas para afrontar esta situación pues, de no hacerlo, habría serias consecuencias sobre el ingreso de los países andinos. Existen muchos sectores que han sido afectados y también beneficiados por la pandemia. Resulta fundamental atender los desafíos del sector agrícola, especialmente aquellos relacionados con su baja productividad. Cabe señalar que, a pesar del entorno, este sector ha crecido. El reposicionamiento de las cadenas globales de valor destaca las oportunidades no aprovechadas por la región andina. Además, el sector servicios, principal empleador de las economías andinas, fue impactado fuertemente por la pandemia. Por otro lado, el sector extractivo ya presentaba retos importantes, aun antes de este proceso de transformación. Esto ocurre en un contexto donde, pese a su crecimiento, la digitalización sigue teniendo grandes rezagos. Más aún, la región se ha caracterizado por presentar retos de desigualdad, que representan un desafío adicional de una transición que va a tener impactos sociales considerables. Este contexto abre oportunidades para la región, pero exige un importante esfuerzo de coordinación de políticas públicas. La región tiene la tarea de diversificarse. Esta publicación presenta recomendaciones al respecto. Al revisar los sectores antes mencionados, resulta fundamental atender los desafíos del sector agrícola, en particular los relacionados con su baja productividad. Existen oportunidades para integrarse a las cadenas globales de valor, pero los países andinos deberán hacer un mejor uso de los tratados comerciales existentes, buscando reducir costos para el comercio. Cabe mencionar que la región tiene espacio para aprovechar las transformaciones que están ocurriendo en el sector servicios. Finalmente, pese a existir desafíos importantes para el sector de industrias extractivas, también se presentan oportunidades para aprovechar este sector como palanca para la transformación productiva. Para lograrlo, es necesario promover la digitalización empresarial y facilitar a las empresas la decisión de qué tecnologías digitales implementar y cómo hacerlo. Cualquier estrategia de transformación productiva debe generar oportunidades para la población. Por tanto, es necesario considerar que el fomentar sectores de la economía más diversos e inclusivos no solo es más equitativo y justo, también es más rentable

Managing buyer experience in a buyer–supplier relationship in MSMEs and SMEs

Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers’ expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer–supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer–supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer–supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers’ technology capabilities (digital readiness, big data analytical capability)

A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction

Traditional credit risk prediction models mainly rely on financial data. However, technological innovation is the main driving force for the development of enterprises in strategic emerging industries, which is closely related to enterprise credit risk. In this paper, a novel prediction framework utilizing technological innovation text mining data and ensemble learning is proposed. The empirical data from China listed enterprises in strategic emerging industries were applied to construct prediction models using the classification and regression tree model, the random forest model and extreme gradient boosting model. The results show that the model uses the technological innovation text mining data proven to have significant predict ability, and top management teamꞌs attention to innovation variables offer the best prediction capacities. This work improves the application value of enterprise credit risk prediction models in strategic emerging industries by embedding the mining of technological innovation text information