Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propagation neural (BPN) network has been applied successfully in many areas with excellent generalization results, such as rule extraction, classification and evaluation. In this paper, a model based on SVM with Gaussian RBF kernel is proposed here for enterprise financial distress evaluation. BPN network is considered one of the simplest and are most general methods used for supervised training of multilayered neural network. The comparative results show that through the difference between the performance measures is marginal; SVM gives higher precision and lower error rates.
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）
Financial Distress Prediction plays a crucial role in the economy by accurately forecasting the number and probability of failing structures, providing insight into the growth and stability of a country's economy. However, predicting financial distress for Small and Medium Enterprises is challenging due to their inherent ambiguity, leading to increased funding costs and decreased chances of receiving funds. While several strategies have been developed for effective FCP, their implementation, accuracy, and data security fall short of practical applications. Additionally, many of these strategie…
University of Pavia（帕维亚大学）；Turku Centre for Computer Science（图尔库计算机科学中心）；Hanken School of Economics（汉肯经济学院）；RiskLab Finland（芬兰风险实验室）
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and sy…
Machine Learning Enabled Early Warning System For Financial Distress Using Real-Time Digital Signals
Gannon University（甘农大学）；University of the Potomac（波托马克大学）；Central Washington University（中央华盛顿大学）；University of Illinois Springfield（伊利诺伊大学斯普林菲尔德分校）；Stony Brook University（石溪大学）；Westcliff University（韦斯特克利夫大学）；Washington University of Science and Technology（华盛顿科技大学）
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
Southwestern University of Finance and Economics（西南财经大学）；Chengdu University（成都大学）；Universität Hamburg（汉堡大学）；The University of Hong Kong（香港大学）；Hunan University of Technology and Business（湖南工业大学）；Xiangjiang Laboratory（湘江实验室）；Emory University（埃默里大学）
Financial Management System for SMEs: Real-World Deployment of Accounts Receivable and Cash Flow Prediction
Doctoral School of Exact and Natural Sciences Jagiellonian University（杰洛尼沃大学精确与自然科学博士学校）；Faculty of Physics, Astronomy and Applied Computer Science, Institute of Applied Computer Science, and Jagiellonian Human-Centered AI Lab Jagiellonian University（杰洛尼沃大学物理、天文学和应用计算机科学学院，应用计算机科学研究所，杰洛尼沃人类中心人工智能实验室）
Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications. With the expansion of inclusive finance, recent attentions are paid to micro and small-sized enterprises (MSEs). Compared with large companies, MSEs present a higher exposure rate to default owing to their insecure financial stability. Conventional efforts learn classifiers from historical data with elaborate feature engineering. However, the main obstacle for MSEs involves severe deficiency in credit-related informat…
Central University of Finance and Economics（中央财经大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are gene…
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
University of California, Irvine（加州大学欧文分校）；Rice University（莱斯大学）；New York University（纽约大学）；University of Chicago（芝加哥大学）；Trine University（特莱恩大学）；Columbia University（哥伦比亚大学）
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, intelligent feedback loops in large-scale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few. Meanwhile, as the value of data is increasingly recognized and monetized, concerns about securing valuable data and risks to individual privacy have been gr…
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become a serious problem currently, i.e., different parties cannot share data with each other. To solve this issue, most research leverages cryptographic techniques to train secure DNN models for multi-parties without compromising their private data. Although such methods have strong security guarantee, they are difficult to scale to deep networks and large datasets due to its high communication and computation complexitie…
Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study
For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experimenting with techniques from Natural Language Processing (Encoder-Decoder LSTMs) and Computer Vision (Dilated CNNs), as well as incorporating transfer learning. A novel contribution of this paper is the application of curriculum learning to neural network models built for time se…
