By Mohammad Asif Ali, PNC Financial Services
Abstract:
In the financial industry, the underwriting process is an essential yet often protracted element of risk assessment. Traditional methods of underwriting are largely reliant on human expertise, rule-based evaluations, and statistical models, which can result in inefficiencies, inconsistencies, and delays in processing. This paper examines the groundbreaking implementation of Agentic Artificial Intelligence (AI) in underwriting, employing large language models (LLMs), retrieval-augmented generation (RAG), and robotic process automation (RPA) to automate and refine decision-making processes. Through empirical validation, we demonstrate that Agentic AI significantly enhances the efficiency of loan processing, reduces bias, and improves the precision of risk assessments. Furthermore, the study compares the efficacy of AI-driven underwriting models against conventional methods, highlighting substantial advancements in processing speed, cost efficiency, and consistency in decision-making. Finally, we explore the challenges related to AI explainability, adherence to regulatory standards, and future prospects for AI-enhanced underwriting.
Keywords: Agentic AI, Artificial Intelligence, Loan Underwriter, Large Language Model(LLM), Automation, Retrieval Augmented Generation (RAG), Risk Assessment
https://www.researchgate.net/publication/389650419_Efficient_Underwriting_using_Agentic_AI
Archived link: https://archive.is/d5otH