This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Paper proposes a method to evaluate SME credit risk using meta paths.
problem Evaluate credit risk of small and medium-sized enterprises with limited data.
method Exploits the representative power of information networks and meta paths to infer SME financial status.
result Meta path feature effectively identifies SMEs with credit risks.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
problem Understanding the dynamics of drug pipelines in global pharmaceutical industry.
method Multilayer network analysis of drug pipeline, global supply chain, and ownership data.
result Proven similarities in knowledge flows between pharmaceutical and AI industries.
Financial institutions face new model risks with AI, requiring enhanced model risk management.
problem New model risks from Generative AI applications in financial institutions.
method Enhanced model risk framework with additional testing and controls.
result Financial institutions need to enhance their model risk management for Generative AI applications.
This paper develops a federated approach to learn Granger causality in interdependent industrial clients.
problem Detecting and quantifying interdependencies in large, complex industrial data.
method Linear state space system framework, federated learning, differential privacy.
result Federated Granger causality learning addresses bandwidth and computational limitations.
This paper surveys enterprise financial risk analysis from Big Data and LLMs perspectives.
problem Predicting future financial risk of enterprises.
method Systematic literature review of enterprise financial risk analysis approaches from Big Data and LLMs perspectives.
result Offers a holistic synthesis of research methods and key insights.
Concurrent engineering taking into account product life-cycle factors seems to be one of the industrial challenges of the next years. Cost estimation and management are two main strategic tasks that imply the possibility of managing costs at the earliest stages of product development. This is why it is indispensable to…
This study identifies financial risk paths in digital-transformed enterprises.
problem Identifying financial risks in digital-transformed enterprises.
method DEMATEL-ISM-MICMAC method.
result Political and economic environment affects enterprise's financial structure.
Analyzes 6M Python notebooks and 2M enterprise DS pipelines to guide investments in data science.
problem Challenges in following the rapidly evolving landscape of data science technologies and applications.
method Downloaded and analyzed over 6M Python notebooks and 2M enterprise DS pipelines, performing statistical and comparative analyses.
result Identifies actionable conclusions for system builders and technology bets for practitioners based on current trends.
This study proposes a new model for predicting financial distress in SMEs using machine learning.
problem Challenges in predicting financial distress for SMEs due to ambiguity and limited data.
method Feature selection algorithm based on element credits and data source collection. Incorporates financial statements, governance qualities, and market data with a Relevant Vector Machine.
result The proposed model improves financial distress prediction efficiency with fewer characteristic factors.
Paper assesses financial potential for enterprise development.
problem Determining financial potential for enterprise development.
method Stages of financial potential assessment based on literature analysis.
result Proposes a mechanism for managing enterprise financial potential.
Paper presents a new method for better financial market forecasting.
problem Traditional investment strategies fail to capture market nuances and risks.
method Combines deep learning, factor integration, and correlated stock analysis.
result Enhanced diversification and performance capture in financial markets.
Study combines intra-risk and contagion risk for SME bankruptcy prediction.
problem Predicting bankruptcy risk of SMEs considering both intra-risk and contagion risk.
method Proposes a novel model using Graph Neural Networks to combine intra-risk and contagion risk.
result Model outperforms state-of-the-art methods in bankruptcy prediction.
Graph neural networks improve SME credit risk assessment.
problem Improving credit risk assessment for small and medium enterprises (SMEs).
method Graph neural networks were used to model the relationships between financial indicators of enterprises, creating a graph structure and embedding representations for credit risk prediction.
result The proposed model accurately predicts enterprise credit levels, demonstrating robustness and effectiveness.
Paper uses ML to predict SME defaults with interpretability.
problem Lack of interpretability in ML models for SME default prediction.
method Model-agnostic approach using Accumulated Local Effects and Shapley values.
result eXtreme Gradient Boosting algorithm provides highest classification power with interpretability.
We present a macroeconomic agent-based model that combines several mechanisms operating at the same timescale, while remaining mathematically tractable. It comprises enterprises and workers who compete in a job market and a commodity goods market. The model is stock-flow consistent; a bank lends money charging interest…
For researching the association between coal enterprise management and return in financial market, this paper applies the method of time difference relevance and PageRank method to seek the leader-index of a stock set containing 21 coal enterprises in A-share market and score those stocks. Based on the return in 2011, …
Margin trading and short selling boost green tech innovation in China.
problem Encouraging green technology innovation in Chinese companies.
method Quasi-experimental research using panel data of Chinese listed companies, double difference model.
result Margin trading and short selling increase green tech innovation significantly.
Study reveals holiday effect on China's time-honored brands, especially alcoholic beverages.
problem Understanding holiday impact on China's time-honored brands.
method Event study using listed companies of China's time-honored brands from 2012-2021.
result Time-honored brand stocks show significant post-holiday effect during Chinese New Year, alcoholic beverages more sensitive.
iConViz helps banks manage default contagion risk in networked loans.
problem Managing default contagion risk in networked loans during economic downturns.
method Developed iConViz, an interactive tool, and a novel metric (contagion effect) to quantify and analyze the risk.
result iConViz facilitates closed-loop analysis and helps avoid ad hoc methods.
Study introduces new financial ratios for better predicting company performance.
problem Lack of progress in predicting company performance and assessing financial risks.
method Developed new financial and macroeconomic ratios, supervised learning models, and Bayesian models.
result New proposed variables improve model accuracy and FNN performs best across multiple tasks.
Taylor's law of temporal fluctuation scaling, variance ∼ a(mean)b, is ubiquitous in natural and social sciences. We report for the first time convincing evidence of a solid temporal fluctuation scaling law in stock illiquidity by investigating the mean-variance relationship of the high-frequency illiquidity o…
A new method to protect enterprise data privacy in AI models.
problem Enterprise data leakage risks in AI models.
method ABack, a training-free mechanism using Hidden State Model.
result Improves privacy utility by up to 15% over strong baselines.
The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively. However, existing machine learning frameworks are limited to sufficiently fulfill the collaboration and management for both data and models. We proposed NSML, a machine learn…
The paper shows supply chain features improve cyber risk prediction.
problem Predicting cyber risk from supply chain attributes.
method Machine learning, external supply chain features, AUC improvement.
result Supply chain network features improve AUC by 2.3%.
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…
Chebyshev polynomials analyze Czech enterprises' stock dynamics.
problem Analyzing stock dynamics of enterprises not following normal distribution.
method Chebyshev polynomial decomposition of stock time series.
result Allows effective analysis of stock dynamics without variance and correlation.
AI-driven framework improves enterprise financial audits and risk identification.
problem Manual auditing is inefficient and limited by data complexity and evolving fraud tactics.
method Machine learning algorithms (SVM, RF, KNN) applied to a dataset of audit project counts, violations, and fraud instances.
result Random Forest achieves best performance with F1-score of 0.9012, identifying fraud and compliance anomalies.
Intel's system identifies and categorizes businesses for sales opportunities.
problem Identifying relevant new markets and customers for large enterprises.
method Mining public business web pages, enriching with external data, and using deep learning.
result Significantly boosts sales personnel's ability to discover new customers and partnerships.
To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security event from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. H…
Being one of the most important factors of economic growth of the country, innovations became one of the key vectors in Russian economic policy. In this field technology parks are one of the most effective instruments which can provide growth of innovative activity in sectors, regions and economies. In this paper, we m…
Generative AI agents improve ERP systems by automating complex financial tasks.
problem Static, rule-based workflows limit adaptability and intelligence in ERP systems.
method Introducing Generative Business Process AI Agents (GBPAs) that integrate generative AI with business process modeling and multi-agent orchestration.
result GBPAs achieve up to 40% reduction in processing time and 94% drop in error rate.
On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported d…
Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make this problem more difficult to solve. Since the guaranteed loan is a debt oblig…
Study on insurance risk management and sustainable development.
problem Lack of attention to non-climate change aspects of sustainable development in insurance.
method Analysis of recent developments and legislative initiatives in insurance risk management.
result Strategies for small- and medium-sized enterprises to manage sustainable development risks.
The basic financial purpose of an enterprise is maximization of its value. Trade credit management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. …
FinanceBench benchmarks LLMs on financial QA, revealing limitations.
problem Evaluating LLMs' performance on financial question answering.
method Developed a comprehensive test suite (FinanceBench) with 10,231 questions, tested 16 models, and manually reviewed answers.
result Existing LLMs have significant limitations for financial QA, especially GPT-4-Turbo.
It was not until the beginning of the 1990s that the effects of information and communication technology on economic growth as well as on the profitability of enterprises raised the interest of researchers. After giving a general description on the relationship between a more intense use of ICT devices and dynamic econ…
The constant growth of the e-commerce industry has rendered the problem of product retrieval particularly important. As more enterprises move their activities on the Web, the volume and the diversity of the product-related information increase quickly. These factors make it difficult for the users to identify and compa…
Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user's dataset is prohibitively expensive. Therefo…
Digital transformation boosts corporate financial asset allocation, especially short-term.
problem Understanding how digital transformation affects corporate financial decisions.
method Fixed-effects models and staggered DID design using A-share listed companies data.
result Digital transformation significantly promotes corporate financial asset allocation, more pronounced in short-term.
SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.
problem AI exclusion of SMEs due to data scale mismatch.
method Bayesian transfer learning with hierarchical pooling and conformal prediction.
result 96.7% AUC on 100 obs SMEs, 24.2 point improvement over logistic regression.
Most of the existing solutions to enterprise threat management are preventive approaches prescribing means to prevent policy violations with varying degrees of success. In this paper we consider the complementary scenario where a number of security violations have already occurred, or security threats, or vulnerabiliti…
New framework uses MABs for better WLAN performance.
problem Maximizing enterprise WLAN performance with complex AP and station interactions.
method Empowered APs and stations with agents using Thompson sampling to learn optimal channel and AP selection.
result Adaptive framework using MABs outperforms static configurations in various scenarios.
This paper establishes a theoretical foundation for super-models via domain adaptation.
problem Reducing computational and data costs in AI for small and medium-sized enterprises.
method Two-stage diffusion process modeling, including pre-training and fine-tuning stages, using the Uhlenbeck-Ornstein process.
result The generalization error of the fine-tuning stage is dominant in domain adaptation.
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Paper proposes a semi-supervised learning approach for automated topic detection in support and feedback data.
problem Manual analysis of large textual feedback and support data is time-consuming and labor-intensive.
method Combines BERT-based multiclassification with a novel PSHTI model for automated topic and sub-topic inference.
result Automated system for better understanding user voice and engagement patterns in enterprise data.
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…