The document-heavy multi-trillion-dollar global insurance industry will benefit greatly from AI/ML’s efficiencies, resolving myriad data quality limitations. The Ag sector should take notice.
Large Language Models and AI can be used to support data integrity while inferring quality standards. Although an evolving area for AI, it will pose incredible business value. AI this:
• Spots data quality errors, models ingest data sets, picks inconsistencies and suggests fixes well ahead of human error rates. • Has models using public data, third party sources or historical claims from client profiles, creating fresh data points. • Can generate data quality rules to improve standards. A high-quality dataset uses AI to extract rules the set is based on and overlay them onto other sets to elevate to that same standard. While not yet 100% accurate, such tools will place insurers ahead of competition.
Actuaries and underwriters benefit greatly, as do data teams, needing to sideline tedium in order to focus on core competencies instead.
