2024/05/07 by Xiangpeng Wan, Haicheng Deng, Wan, Xiangpeng +5
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FinTech, Crowdfunding, Digital Finance #Financial Distress and Bankruptcy Prediction #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2405.04294
openalex publication_date 2024/05/07 · openalex created_date 2024/05/11 · openalex updated_date 2026/07/28
Structured finance, which involves restructuring diverse assets into securities like MBS, ABS, and CDOs, enhances capital market efficiency but presents significant due diligence challenges. This study explores the integration of artificial intelligence (AI) with traditional asset review processes to improve efficiency and accuracy in structured finance. Using both open-sourced and close-sourced large language models (LLMs), we demonstrate that AI can automate the verification of information between loan applications and bank statements effectively. While close-sourced models such as GPT-4 show superior performance, open-sourced models like LLAMA3 offer a cost-effective alternative. Dual-agent systems further increase accuracy, though this comes with higher operational costs. This research highlights AI's potential to minimize manual errors and streamline due diligence, suggesting a broader application of AI in financial document analysis and risk management.