Insurance Data Extraction with LLMs (www.adaptional.com)

LLMs like ChatGPT, Claude, and Gemini can jump-start insurance field extraction, but real-world deployment requires substantial engineering. The core task splits into single-entity extractions (one record per submission, e.g., overall policy limits) and list-entity extractions (repeated items, e.g., property schedules). Single-entity is relatively straightforward; list-entity is hard because inputs are messy—emails, PDFs and inconsistent labels—and the system must decide which items are duplicates, which are distinct, and how to reconcile conflicting values into a single canonical record. Properties are especially challenging because there’s no universal identifier: addresses are a useful proxy but imperfect (multiple properties per address, variant naming). This motivates parallel processing across addresses but requires sophisticated within-address deduplication. Practical approaches compare key fields (TIV, BPP), apply weighted scoring, set thresholds, and model human error and mixed signals to decide merges. The implication for AI/ML is clear: LLMs are useful components but production-grade extraction demands hybrid pipelines—entity-resolution algorithms, deterministic rules, uncertainty quantification, and human-in-the-loop reconciliation. The last 10–20% of accuracy (edge cases and conflicting data) is exponentially harder and is where research and engineering investment in robust canonicalization, evaluation metrics, and scalable record linkage pays off.