Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

• URL: http://arxiv.org/abs/2609.15744v1
• Date: Mon, 14 Sep 2026 15:35:41 GMT
• Title: Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
• Abstract summary: Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays.<n>This paper proposes a credit risk early warning system based on heterogeneous information fusion.
• Score: 15.54106981546517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability.

Related papers

• An approach to systemic risks of AI through the lens of emergence, collective action problems, and externalities [41.99844472131922]
  There is no generally accepted definition of systemic risks in general and for AI in particular.<n>The application of the systemic risk approach to human rights or fundamental rights, like in the EU AI Act, is relatively new.<n>We argue that some concepts so far have not sufficiently take complexity and emergence into account.
  arXiv  Detail & Related papers  (2026-07-20T17:12:04Z)
• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets [44.76567557906836]
  A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data.<n>This paper introduces a novel framework, named ProteuS, for generating semi-synthetic financial time series with pre-defined structural breaks.<n>An analysis of the generated data confirms the complexity of the task, revealing significant overlap between the different market states.
  arXiv  Detail & Related papers  (2025-08-30T21:01:47Z)
• Information Retrieval in the Age of Generative AI: The RGB Model [77.96475639967431]
  This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.<n>We propose a model to characterize the generation, indexing, and dissemination of information in response to new topics.<n>Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation.
  arXiv  Detail & Related papers  (2025-04-29T10:21:40Z)
• Utilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation [10.464514412664862]
  Financial risks can propagate across both tightly coupled temporal and spatial dimensions.<n>Risks embedded in unlabeled data are often difficult to detect.<n>We introduce GraphShield, a novel approach with three key innovations.
  arXiv  Detail & Related papers  (2025-02-18T08:09:05Z)
• VulRG: Multi-Level Explainable Vulnerability Patch Ranking for Complex Systems Using Graphs [20.407534993667607]
  This work introduces a graph-based framework for vulnerability patch prioritization.<n>It integrates diverse data sources and metrics into a universally applicable model.<n> refined risk metrics enable detailed assessments at the component, asset, and system levels.
  arXiv  Detail & Related papers  (2025-02-16T14:21:52Z)
• Robust Graph Neural Networks for Stability Analysis in Dynamic Networks [16.077138803931295]
  This paper explores the economic risk identification algorithm based on the graph neural network (GNN) algorithm. It aims to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market.
  arXiv  Detail & Related papers  (2024-10-29T06:11:36Z)
• Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets [9.599753686171217]
  This paper designs and optimize a risk monitoring system based on big data and machine learning. It effectively integrates large-scale financial data and advanced machine learning algorithms. Research findings demonstrate that the system significantly enhances efficiency and accuracy in risk management.
  arXiv  Detail & Related papers  (2024-07-28T00:04:34Z)
• Graph Dimension Attention Networks for Enterprise Credit Assessment [40.87056211723355]
  We propose a novel architecture named Graph Dimension Attention Network (GDAN) GDAN incorporates a dimension-level attention mechanism to capture fine-grained risk-related characteristics. We explore the interpretability of the GNN-based method in financial scenarios and propose a data-centric explainer for GDAN, called GDAN-DistShift.
  arXiv  Detail & Related papers  (2024-07-16T11:24:28Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework [27.025720728622897]
  We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network.
  arXiv  Detail & Related papers  (2023-12-27T20:09:57Z)
• Risk-Driven Design of Perception Systems [47.787943101699966]
  It is important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-driven approach to designing perception systems that accounts for the effect of perceptual errors on the performance of the fully-integrated, closed-loop system. We evaluate our techniques on a realistic vision-based aircraft detect and avoid application and show that risk-driven design reduces collision risk by 37% over a baseline system.
  arXiv  Detail & Related papers  (2022-05-21T21:14:56Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.