Translating Expert Intuition into Quantifiable Features: Encode Investigator Domain Knowledge via LLM for Enhanced Predictive Analytics

TL;DR

This paper introduces a framework using Large Language Models to convert investigator expertise into quantifiable features, significantly improving predictive analytics by integrating human domain knowledge into machine learning models.

Contribution

It presents a novel method for encoding expert insights into structured features using LLMs, enhancing model performance across multiple prediction tasks.

Findings

Improved risk assessment accuracy

Enhanced decision-making performance

Effective integration of human expertise into ML models

Abstract

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.