Credit Network Modeling and Analysis via Large Language Models

2025-11-02

AI Summary

Financial systemic risk analysis lacks scalable, automated methods for constructing and optimizing credit networks from unstructured corporate financial reports. Method: This paper proposes a large language model (LLM)-based framework for end-to-end credit network construction and optimization. It introduces the first application of LLMs to credit relationship extraction, textual inconsistency detection, and human-in-the-loop verification; integrates graph modeling with combinatorial optimization to support network generation, structural analysis, and strategic recommendations—including portfolio compression and debt cancellation. Contribution/Results: Evaluated on both synthetic and real-world financial texts, the framework generates semantically coherent and structurally sound credit networks. Recommended strategies significantly enhance network robustness—measured proxy by aggregate corporate total assets—demonstrating improved resilience against cascading defaults. The approach establishes a novel paradigm for systemic risk assessment and automated financial regulation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We investigate the application of large language models (LLMs) to construct credit networks from firms' textual financial statements and to analyze the resulting network structures. We start with using LLMs to translate each firm's financial statement into a credit network that pertains solely to that firm. These networks are then aggregated to form a comprehensive credit network representing the whole financial system. During this process, the inconsistencies in financial statements are automatically detected and human intervention is involved. We demonstrate that this translation process is effective across financial statements corresponding to credit networks with diverse topological structures. We further investigate the reasoning capabilities of LLMs in analyzing credit networks and determining optimal strategies for executing financial operations to maximize network performance measured by the total assets of firms, which is an inherently combinatorial optimization challenge. To demonstrate this capability, we focus on two financial operations: portfolio compression and debt removal, applying them to both synthetic and real-world datasets. Our findings show that LLMs can generate coherent reasoning and recommend effective executions of these operations to enhance overall network performance.

Problem

• Constructing credit networks from financial statements using LLMs
• Detecting inconsistencies in financial statements during network aggregation
• Optimizing financial operations through LLM reasoning on network structures

Innovation

• LLMs construct credit networks from financial statements
• LLMs detect inconsistencies and involve human intervention
• LLMs optimize financial operations through combinatorial reasoning