  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.
