Credit Network Modeling and Analysis via Large Language Models

TL;DR

This paper explores how large language models can construct and analyze credit networks from financial statements, demonstrating their ability to reason about and optimize financial operations within these networks.

Contribution

It introduces a novel method for translating financial statements into credit networks using LLMs and evaluates their reasoning and optimization capabilities in financial contexts.

Findings

LLMs effectively translate financial statements into credit networks.

LLMs can reason about and optimize financial operations like portfolio compression.

The approach works on both synthetic and real-world datasets.

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.