Use of Artificial Intelligence in the insurance market: an approach based on the level of disclosure in financial reports
DOI:
https://doi.org/10.23925/2446-9513.2025v12id69774Keywords:
Artificial Intelligence, Insurance Market, Disclosure TheoryAbstract
In view of technological advancements and the increasing use of Artificial Intelligence (AI) across various economic sectors, this study aimed to investigate the use of AI by Brazilian insurance companies based on its disclosure in financial statements, management reports, and sustainability reports. A sample of 30 insurers, representing 80.8% of the total market premium volume as of September 30, 2024, was selected. Financial statements dated December 31, 2023, and June 30, 2024, as well as the sustainability reports dated December 31, 2023, were analyzed. The results indicated that only nine insurers disclosed the use of AI in their reports. Among the disclosed AI applications were improving customer and broker experience, developing products and solutions, achieving operational excellence and digitalization, fostering an AI-driven culture, and attracting new talent.
References
ABAKARIM, Y.; LAHBY, M.; ATTIOU, A.A. Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds, Appl. Syst. Innovation, v. 6, n.1, 20, 2023.
ADEOYE, Omotayo Bukola et al. Integrating artificial intelligence in personalized insurance products: a pathway to enhanced customer engagement. International Journal of Management & Entrepreneurship Research, v. 6, n. 3, p. 502-511, 2024.
ALPAYDIN, E. Introduction to Machine Learning. 4th ed. MIT Press, 2020.
ANAGNOSTE, S. Robotic Automation Process - The next major revolution in terms of backoffice operations improvement. Proceedings of the International Conference on Business Excellence, v.11, n.1, p. 676–686, 2017. Disponível em https://doi.org/10.1515/picbe-2017-0072. Acesso em 12/12/2024.
BHAMBRI, P.; RANI, S.; PAREEK, P.K. Financial innovations: Intelligent automation in finance and insurance sectors, Hyperautomation in Business and Society, p 226 -243, 2024.
BARDIN, L. Análise de conteúdo. 3º. Edição Lisboa: Edições 70, 2004.
BARROS, Pedro Henrique Batista de; FREITAS JUNIOR, Adirson Maciel de. Combinando Inteligência Artificial e imagens de satélite para a previsão de sinistros agrícolas: Uma nota. Revista Brasileira de Economia, v. 77, p. e012023, 2023.
BELLI, V.; MEDEIROS, Lucas de; PRADO JUNIOR, Tarcis do. Substituição de pessoas por máquinas e o uso de inteligência artificial pelo mercado segurador. Revista Brasileira de Risco e Seguro, v. 14, n. 24, 2018.
BROWN, Sara. Machine Learning, explained. Disponível em: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained . Acesso em: 12/12/24.
BUSHMAN, R. M.; SMITH, A. J. Financial Accounting Information and Corporate Governance. Journal of Accounting and Economics, v. 32, n. 1-3, p. 237-333, 2001.
CASTRO, C. M. Estrutura e apresentação de publicações científicas. São Paulo: McGraw-Hill, 1976.
CEYLAN, I.E., The effects of artificial intelligence on the insurance sector: emergence, applications, challenges, and opportunities. Accounting. Finance. Sustainability. Governance. Fraud, p. 225–241, 2022.
CHOI, E. P. H. Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Education Today, 125, 2023.
CUMMINGS, J.; HARTMAN, B. Using machine learning to better model long-term care insurance claims. North American Actuarial Journal, v. 26 n. 3, p. 470–483, 2022.
DAVE, H. S.; PATWA, J. R.; PANDIT, N. B. Facilitators and barriers to participation of the private sector health facilities in health insurance & government led schemes in India. Clinical Epidemiology and Global Health, v.10, 2021.
DOWLING, M.; LUCEY, B. ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 2023.
GHOSH, S. Artificial Intelligence in Insurance: Risk Assessment, Fraud Detection, and Claims Management. Wiley, 2021.
GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. MIT Press, 2016.
GUPTA, Somya et al. Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework. Research in International Business and Finance, v. 63, p. 101757, 2022.
HEALY, P. M.; PALEPU, K. G. Information Asymmetry, Corporate Disclosure, and the Capital Markets: A Review of the Empirical Disclosure Literature. Journal of Accounting and Economics, v. 31, n. 1-3, p. 405-440, 2001.
HERRMANN, H.; MASAWI, B. Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review, Strategic Change, V. 31, p.549–569, 2022.
JURAFSKY, D.; MARTIN, J. H. Speech and Language Processing. 3rd ed. Pearson, 2023.
KUNA, Siva Sarana. The Role of Natural Language Processing in Enhancing Insurance Document Processing. Journal of Bioinformatics and Artificial Intelligence, v. 3, n. 1, p. 289-335, 2023.
LAKATOS, E. M.; MARCONI, M. A. Fundamentos metodologia científica. 4.ed. São Paulo: Atlas, 2001.
LEE, I.; SHIN, Y.J., Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, V. 63, p. 150-170, 2020.
LI, X. et al. Applications of Computer Vision and Natural Language Processing in Insurance: A Systematic Review. Journal of Artificial Intelligence Research, v. 70, p. 56-72, 2023.
LISSACK, M.; MEAGHER, B. (2024). Responsible Use of Large Language Models: An Analogy with the Oxford Tutorial System. The Journal of Design, Economics, and Innovation, Vol. 10, No. 4, 2024.
MAEHASHI, K.; SHINTANI, M. Macroeconomic forecasting using factor models and machine learning: an application to Japan. Journal of the Japanese and International Economies, p.58, 2020.
MEIRYANI, A. et al. ANALYSIS ROLE OF ROBOTIC PROCESS AUTOMATION IN ACCOUNTING AND BUSINESS, Journal of Theoretical and Applied Information Technology, v.101. n 11, 2023.
RAMALHOSO, Wellington. Uso de inteligência artificial por seguradoras torna o segmento ainda mais eficiente. Notícia publicada no Estado de São Paulo em 11/12/2024. Disponível em: https://www.estadao.com.br/economia/negocios/uso-de-inteligencia-artificial-por-seguradoras-torna-o-segmento-ainda-mais-eficiente/. Acesso em: 18/12/2024.
RAWAT, S.; RAWAT, A.; KUMAR, D.; SABITHA, A. S. Application of machine learning and data visualization techniques for decision support in the insurance sector, International Journal of Information Management Data Insights, v. 1, 2021.
RESSEL, J.; VOLLER, M.; MURPHY, F.; MULLINS, M. Addressing the notion of trust around ChatGPT in the high-stakes use case of insurance. Technology in Society, v. 78, 2024.
RUSSELL, S.; NORVIG, P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson, 2020.
SHI, P.; ZHANG, W.; SHI, K. Leveraging weather dynamics in insurance claims triage using deep learning. Journal of the American Statistical Association, v. 119, n. 546, p. 825–838, 2024.
SMITH, R. An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International Conference on Document Analysis and Recognition, 2007.
SUSEP. BASE SESP (2024). Disponível em: https://www2.susep.gov.br/menuestatistica/SES/balanco.aspx?tipo=seg&id=14 Acesso em 10/12/2024.
VERRECCHIA, R. E. Essays on Disclosure. Journal of Accounting and Economics, v. 32, n. 1-3, p. 97-180, 2001.
YE, Q.; DOERMANN, D. Text detection and recognition in imagery: A survey. IEEE transactions on pattern analysis and machine intelligence, v. 37, n. 7, p. 1480–1500, 2015.
ZÖLLER, M.; HUBER, M. F. Benchmark and Survey of Automated Machine Learning Frameworks, Journal of Artificial Intelligence Research, V. 70, p. 409-472, 2021.
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Copyright (c) 2025 Fabiana Lopes da Silva, Betty Lilian Chan, Sonia Rosa Arbues Decoster
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