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@article{184093,
        author = {Sita Rama Praveen Madugula and Nihar Malali},
        title = {SYNTHETIC DATA AND GENERATIVE MODELS FOR LIFE INSURANCE ANALYTICS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {436-448},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184093},
        abstract = {The integration of synthetic data into actuarial science has gained momentum as insurers seek to balance data utility, confidentiality, and methodological rigour. Generative models, particularly generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, provide robust frameworks for synthesising policyholder records and event histories while safeguarding sensitive information. These approaches offer significant opportunities in rare-event simulation, stress testing annuity guarantees, and enabling cross-institutional benchmarking with privacy guarantees. However, actuarial adoption requires rigorous validation to ensure that synthetic cohorts preserve mortality gradients, lapse–interest rate dependencies, and tail adequacy, which are critical to solvency assessments. Moreover, issues of fairness, regulatory acceptance, and interpretability add complexity to their use in insurance practice. This survey reviews methodological advances in generative models for structured and survival-type data, examines actuarial validation criteria, and explores complementary rare-event simulation techniques to bolster tail realism. It also identifies pathways for regulatory adoption by adapting transparency and readiness frameworks from safety-critical domains. Findings suggest that diffusion models excel in dependence preservation. At the same time, adversarial methods remain strong in high-dimensional synthesis when coupled with regularization, and VAEs provide tractable density estimations that are advantageous for scenario testing. Nevertheless, all methods face limitations in capturing extreme quantiles, underscoring the need for hybrid approaches. The review concludes with a research agenda focused on tail-aware training objectives, causally anchored generative designs, federated benchmarking, and the development of governance artefacts for supervisory review.},
        keywords = {Synthetic data; Generative models; Life insurance analytics; Rare-event simulation; Privacy-preserving benchmarking; Regulatory acceptance.},
        month = {September},
        }