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

Risk guarantee prediction in networked-loans

© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved. The guaranteed loan is a debt obligation promise that if one corporation gets trapped in risks, its guarantors will back the loan. When more and more companies involve, they subsequently form complex networks. Detecting and predicting risk guarantee in these networked-loans is important for the loan issuer. Therefore, in this paper, we propose a dynamic graph-based attention neural network for risk guarantee relationship prediction (DGANN). In particular, each guarantee is represented as an edge in dynamic loan networks, while companies are denoted as nodes. We present an attention-based graph neural network to encode the edges that preserve the financial status as well as network structures. The experimental result shows that DGANN could significantly improve the risk prediction accuracy in both the precision and recall compared with state-of-the-art baselines. We also conduct empirical studies to uncover the risk guarantee patterns from the learned attentional network features. The result provides an alternative way for loan risk management, which may inspire more work in the future

Regulating Systemic Risk: Towards an Analytical Framework

The global financial crisis demonstrated the inability and unwillingness of financial market participants to safeguard the stability of the financial system. It also highlighted the enormous direct and indirect costs of addressing systemic crises after they have occurred, as opposed to attempting to prevent them from arising. Governments and international organizations are responding with measures intended to make the financial system more resilient to economic shocks, many of which will be implemented by regulatory bodies over time. These measures suffer, however, from the lack of a theoretical account of how systemic risk propagates within the financial system and why regulatory intervention is needed to disrupt it. In this Article, we address this deficiency by examining how systemic risk is transmitted. We then proceed to explain why, in the absence of regulation, market participants cannot be relied upon to disrupt or otherwise limit the transmission of systemic risk. Finally, we advance an analytical framework to inform systemic risk regulation

Community Structure and Market Outcomes: A Repeated Games in Networks Approach

Consider a large market with asymmetric information, in which sellers choose whether to cooperate or deviate and ?cheat?their buyers, and buyers decide whether to re-purchase from di¤erent sellers. We model active trade relationships as links in a buyer-seller network and suggest a framework for studying repeated games in such networks. In our framework, buyers and sellers have rich yet incomplete knowledge of the network structure; allowing us to derive meaningful conditions that determine whether a network is consistent with trade and cooperation between every buyer and seller that are connected. We show that three network features reduce the minimal discount factor necessary for sustaining cooperation: moderate competition, sparseness, and segregation. We ? nd that the incentive constraints rule out networks that maximize the volume of trade and that the constrained trade maximizing networks are in between ?old world? segregated and sparse networks, and a ?global market?Buyer-Seller networks; repeated games; moral hazard;asymmetric information; trust; cooperation; institutions

Graph Learning and Its Applications: A Holistic Survey

Graph learning is a prevalent domain that endeavors to learn the intricate relationships among nodes and the topological structure of graphs. These relationships endow graphs with uniqueness compared to conventional tabular data, as nodes rely on non-Euclidean space and encompass rich information to exploit. Over the years, graph learning has transcended from graph theory to graph data mining. With the advent of representation learning, it has attained remarkable performance in diverse scenarios, including text, image, chemistry, and biology. Owing to its extensive application prospects, graph learning attracts copious attention from the academic community. Despite numerous works proposed to tackle different problems in graph learning, there is a demand to survey previous valuable works. While some researchers have perceived this phenomenon and accomplished impressive surveys on graph learning, they failed to connect related objectives, methods, and applications in a more coherent way. As a result, they did not encompass current ample scenarios and challenging problems due to the rapid expansion of graph learning. Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning. Specifically, we commence by proposing a taxonomy from the perspective of the composition of graph data and then summarize the methods employed in graph learning. We then provide a detailed elucidation of mainstream applications. Finally, based on the current trend of techniques, we propose future directions.Comment: 20 pages, 7 figures, 3 table

Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms

The rise of “Big Data” analytics in the private sector poses new challenges for privacy advocates. Through its reliance on existing data and predictive analysis to create detailed individual profiles, Big Data has exploded the scope of personally identifiable information (“PII”). It has also effectively marginalized regulatory schema by evading current privacy protections with its novel methodology. Furthermore, poor execution of Big Data methodology may create additional harms by rendering inaccurate profiles that nonetheless impact an individual’s life and livelihood. To respond to Big Data’s evolving practices, this Article examines several existing privacy regimes and explains why these approaches inadequately address current Big Data challenges. This Article then proposes a new approach to mitigating predictive privacy harms—that of a right to procedural data due process. Although current privacy regimes offer limited nominal due process-like mechanisms, a more rigorous framework is needed to address their shortcomings. By examining due process’s role in the Anglo-American legal system and building on previous scholarship about due process for public administrative computer systems, this Article argues that individuals affected by Big Data should have similar rights to those in the legal system with respect to how their personal data is used in such adjudications. Using these principles, this Article analogizes a system of regulation that would provide such rights against private Big Data actors