Artificial intelligence in financial institutions: modelling transparency, competence, trust and conflict resolution in the Indian context

Purpose This study aims to investigate the role of the regulatory environment in influencing how the perceived transparency and competence of artificial intelligence (AI) systems affect trust in those systems and conflict resolution ability in India’s financial institutions. It examines the role of trust as an intermediary variable between these AI system attributes and organisational outcomes by employing theories that include trust theory, signalling theory, social exchange theory, the technology acceptance model and institutional theory. Design/methodology/approach Utilizing survey data from 885 employees working in both public and private sector banks across India, collected through a multi-stage sampling method, this study employed structural equation modelling (SmartPLS) to evaluate the conceptual model. The analysis focused on examining both the direct and indirect effects, as well as the mediation of relationships among AI attributes, trust, the regulatory environment and conflict resolution outcomes. Findings The results indicate that perceived transparency and competence are significant predictors of trust in AI systems, and that trust in AI systems is a positive predictor of conflict resolution effectiveness. In addition, trust is an important mediator between AI attributes and operational outcomes. Finally, the regulatory environment positively moderates the relationship between transparency and trust, highlighting the importance of institutional legitimacy and safeguards in fostering employee confidence in AI systems. Research limitations/implications These results have implications for developing AI systems that emphasise transparency, competence and trustworthiness. Regulatory bodies, including the RBI, must develop stronger governance frameworks, oversight mechanisms and AI-literacy initiatives to foster greater trust in AI systems and encourage their institutional adoption. Finally, this study presents practical advice for developing responsible and trustworthy AI systems in a highly regulated financial ecosystem. Originality/value To the best of the authors’ knowledge, this study is one of the first empirical studies in the Indian banking context to demonstrate the causal links between AI system attributes, trust mechanisms, regulatory variables and conflict resolution outcomes. By combining micro-level trust dynamics with macro-level institutional influences, this study provides a new, multi dimensional perspective on the use of AI in regulated financial service environments and expands our theoretical knowledge regarding the use of trust-based collaborative efforts between humans and AI systems.