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@article{184272,
        author = {Sita Rama Praveen Madugula},
        title = {MULTI-MODAL DATA FUSION FOR LIFE AND ANNUITY RISK MODELLING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {976-982},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184272},
        abstract = {The life and annuity insurance sector is undergoing a profound digital transformation as data sources proliferate across structured policy records, unstructured narratives, wearable devices, and macroeconomic indicators. Traditional actuarial models, while robust, are increasingly limited in capturing the full spectrum of risk embedded in these heterogeneous datasets. Multi-modal data fusion offers a systematic approach to integrate such diverse inputs into unified machine learning models, enabling richer insights into underwriting, fraud detection, and long-term risk assessment. This survey reviews the current state of multi-modal fusion architectures, including early, intermediate, late, and hybrid strategies, and evaluates their applicability to life and annuity risk modelling. It further examines the role of data harmonisation, interpretability, and regulatory compliance as both enablers and constraints of adoption. Case applications are illustrated through underwriting enhancements, fraud detection improvements, and capital requirement modelling. The study concludes by addressing outstanding challenges in explainability, ethical governance, and data quality, while highlighting opportunities presented by emerging paradigms such as federated learning, generative modelling, and hybrid actuarial–AI systems.},
        keywords = {Multi-modal data fusion; Life insurance; Annuity modelling; Machine learning; Underwriting; Fraud detection; Risk assessment; Explainable AI; Data harmonisation.},
        month = {September},
        }