  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.
