Fusing LLMs and KGs for Formal Causal Reasoning behind Financial Risk Contagion

TL;DR

This paper introduces RC2R, a novel model that combines large language models and financial knowledge graphs to perform formal causal reasoning for understanding and predicting risk contagion in financial systems.

Contribution

The paper presents a new fusion framework integrating LLMs and KGs for causal reasoning, addressing the causal parrot problem and enhancing risk prediction and interpretability.

Findings

Outperforms state-of-the-art models in prediction accuracy

Demonstrates strong out-of-distribution generalization

Provides detailed causal explanations via visualizations

Abstract

Financial risks trend to spread from one entity to another, ultimately leading to systemic risks. The key to preventing such risks lies in understanding the causal chains behind risk contagion. Despite this, prevailing approaches primarily emphasize identifying risks, overlooking the underlying causal analysis of risk. To address such an issue, we propose a Risk Contagion Causal Reasoning model called RC2R, which uses the logical reasoning capabilities of large language models (LLMs) to dissect the causal mechanisms of risk contagion grounded in the factual and expert knowledge embedded within financial knowledge graphs (KGs). At the data level, we utilize financial KGs to construct causal instructions, empowering LLMs to perform formal causal reasoning on risk propagation and tackle the "causal parrot" problem of LLMs. In terms of model architecture, we integrate a fusion module that aligns tokens and nodes across various granularities via multi-scale contrastive learning, followed by the amalgamation of textual and graph-structured data through soft prompt with cross multi-head attention mechanisms. To quantify risk contagion, we introduce a risk pathway inference module for calculating risk scores for each node in the graph. Finally, we visualize the risk contagion pathways and their intensities using Sankey diagrams, providing detailed causal explanations. Comprehensive experiments on financial KGs and supply chain datasets demonstrate that our model outperforms several state-of-the-art models in prediction performance and out-of-distribution (OOD) generalization capabilities. We will make our dataset and code publicly accessible to encourage further research and development in this field.