2024/05/11 by Phoebe Jing, Jing, Phoebe, Yijing Gao +5
Business, Management and Accounting · Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2405.08017
openalex publication_date 2024/05/11 · openalex created_date 2024/05/16 · openalex updated_date 2026/07/28
In the realm of predictive analytics, the nuanced domain knowledge of investigators often remains underutilized, confined largely to subjective interpretations and ad hoc decision-making. This paper explores the potential of Large Language Models (LLMs) to bridge this gap by systematically converting investigator-derived insights into quantifiable, actionable features that enhance model performance. We present a framework that leverages LLMs' natural language understanding capabilities to encode these red flags into a structured feature set that can be readily integrated into existing predictive models. Through a series of case studies, we demonstrate how this approach not only preserves the critical human expertise within the investigative process but also scales the impact of this knowledge across various prediction tasks. The results indicate significant improvements in risk assessment and decision-making accuracy, highlighting the value of blending human experiential knowledge with advanced machine learning techniques. This study paves the way for more sophisticated, knowledge-driven analytics in fields where expert insight is paramount.