Qualitative case study explores AI's role in improving performance indicators at LIC, suggesting significant operational benefits.
Purpose: The given paper explores how Life Insurance Corporation of India (LIC) can use automated integrated reporting systems, which are facilitated by artificial intelligence (AI) tools, to find the balance between financial and non-financial performance indicators. The research aims to comprehend the way automation based on AI can be used to improve the quality of reporting, decision-making, and communication with stakeholders, which is consistent with the multi-capital model of the value creation that lies at the core of the integrated reporting (Adams, 2017)1. Design/methodology/approach: The research follows the qualitative and exploratory case study design grounded on the analysis of secondary data, including corporate reports, industry surveys, and scholarly literature (Creswell and Poth, 2018)2. It analyzes the digital transformation of LIC in the theoretical perspectives of integrated reporting and technological innovation in financial services. Findings: The findings suggest that AI-driven automated reporting at LIC has led to a tremendous improvement in the performance of the operations, including a reduction in claim settlement time by 35, underwriting error reduction by 15, and client satisfaction improvement by 80-90. The unification of such tools as RPA and machine learning allows achieving the balance in reporting of financial and non-financial indicators, which directly increases operational efficiency and customer experience (Davenport and Ronanki, 2018)3. Originality/value: The study does present original research findings by analyzing the intersection of automation and integrated reporting in a large state-owned insurer of a developing economy, which has not been well-represented in the existing literature (Liu and Vasarhelyi, 2020)4. It offers a realistic, scalable framework of automated integrated reporting, providing practitioners and regulators in the industry with practical advice.
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Mehta et al. (2026) studied this question.
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