TRAM-FIN: A Transformer-Based Real-time Assessment Model for Financial Risk Detection in Multinational Corporate Statements

Abstract

This paper introduces TRAM-FIN, a novel transformer-based model for real-time financial risk detection in multinational corporate statements. Automated risk assessment across diverse regulatory environments presents significant challenges due to linguistic variations, temporal dynamics, and complex interdependencies within financial data. TRAM-FIN addresses these challenges through a specialized architecture incorporating cross-lingual processing modules, financial entity recognition, and temporal pattern analysis. The model implements a hierarchical risk classification framework spanning financial, operational, compliance, and strategic risk categories. Experimental evaluation conducted on a comprehensive dataset of 1,834 financial reports from 157 multinational corporations across 12 countries demonstrates TRAM-FIN's superior performance, achieving an F1-score of 0.892—a 7.9% improvement over existing approaches. Ablation studies confirm the critical contribution of temporal analysis components (+8.9% F1-score) and cross-lingual modules (+7.6% F1-score). The architecture maintains consistent performance across multiple languages with variance below 4.3%. TRAM-FIN addresses critical needs in cross-border financial supervision through unified analytical capabilities that enhance regulatory coordination while reducing compliance burdens. The system's real-time processing capabilities and explainable risk assessments offer significant advantages for financial monitoring within increasingly complex global markets.