THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE ANNUITY SALES BUSINESS PROCESSES OF INDEPENDENT INSURANCE AGENTS
DOI:
https://doi.org/10.20998/2413-3000.2025.11.2Keywords:
business processes; artificial intelligence; machine learning; annuities; independent insurance agents; lead generation; customer needs identification; recommender systems; automation; explainable AI; digital transformationAbstract
This article presents a systematic analysis of the application of artificial intelligence (AI) in the business processes of annuity sales by independent insurance agents and proposes a process-based decomposition of the full customer interaction lifecycle. The following sequential stages are identified: lead generation; client discovery and needs assessment; product research and carrier selection; proposal presentation and consultation; application submission and underwriting; policy issuance and delivery; and post-sale engagement and retention. For each stage, a “mapping” of relevant classes of AI models and methods and their typical functions in decision support and automation is provided. A synthesis of contemporary research shows that analytical models (ML-based scoring, classification, and ensemble approaches) improve lead qualification, customer behavior forecasting, and risk assessment; generative and NLP/LLM-based solutions support work with unstructured data (call transcripts, chat transcripts, correspondence, and documents), enabling parameter extraction, summarization, and the preparation of personalized advisory materials; and agent-based approaches orchestrate workflows within CRM systems and document management. Quantitative indicators of the prevalence of model usage across process stages are presented, reflecting the uneven technological maturity of available solutions. Key implementation barriers are identified, including data fragmentation and heterogeneity, algorithmic bias, limited transparency and explainability, regulatory requirements imposed by the SEC and NAIC, and the complexity of CRM integration. Directions for future research are outlined, including the standardization of approaches, the advancement of explainable AI, and the quantitative assessment of the balance between human expertise and automation at different stages of annuity sales.
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