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<p>This study evaluates how ESG strategic intensity and AI capability shape risk-adjusted lending performance in ASEAN-5 banks, grounding the model in the Resource-Based View (RBV) and Institutional Theory to explain how internal capabilities and external regulatory forces interact. Using a cross-sectional survey of 486 banking professionals from 62 listed commercial banks across Indonesia, Malaysia, the Philippines, Singapore, and Thailand, relationships were estimated via PLS-SEM with standard robustness checks, including multicollinearity, reliability, validity, and predictive relevance, while mediation and moderation were assessed through bootstrapped indirect effects and interaction terms. The results show that ESG strategic intensity directly improves risk-adjusted lending performance, while AI capability influences performance only indirectly. Both ESG and AI strongly enhance credit-risk discipline, which itself is a key driver of lending performance. ESG retains a direct path to performance while also working through credit-risk discipline, its effect reflects partial mediation. In contrast, the effect of AI operates entirely through credit-risk discipline, indicating full mediation. Regulatory pressure strengthens the influence of both ESG and AI on credit-risk discipline, demonstrating that stricter supervisory environments amplify the translation of sustainability and technological capabilities into more disciplined lending practices. These findings underscore CRD as the operational hinge through which sustainability and digital capabilities are converted into superior lending outcomes, highlighting the importance of governed data pipelines, explainable risk models, and effective early-warning mechanisms, supported by aligned managerial incentives and supervisory expectations. At the societal level, disciplined ESG- and AI-enabled lending reduces information frictions, supports equitable credit access for credible SMEs and households, stabilizes credit cycles, and mitigates adverse selection when paired with safeguards on privacy, transparency, and bias control. Overall, the study offers an integrated, theory-driven, institution-level assessment of how ESG and AI capabilities translate into measurable performance within a multi-country ASEAN context, clarifying when and how strategic and regulatory forces jointly improve credit outcomes.</p>

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ESG strategic intensity and AI capability impact on risk-adjusted lending performance; mediating role of credit-risk discipline in ASEAN banks

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  • 出版模式：

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  • 发表日期：

    2026年1月2日

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  • Mohammed R. M. Salem

摘要

This study evaluates how ESG strategic intensity and AI capability shape risk-adjusted lending performance in ASEAN-5 banks, grounding the model in the Resource-Based View (RBV) and Institutional Theory to explain how internal capabilities and external regulatory forces interact. Using a cross-sectional survey of 486 banking professionals from 62 listed commercial banks across Indonesia, Malaysia, the Philippines, Singapore, and Thailand, relationships were estimated via PLS-SEM with standard robustness checks, including multicollinearity, reliability, validity, and predictive relevance, while mediation and moderation were assessed through bootstrapped indirect effects and interaction terms. The results show that ESG strategic intensity directly improves risk-adjusted lending performance, while AI capability influences performance only indirectly. Both ESG and AI strongly enhance credit-risk discipline, which itself is a key driver of lending performance. ESG retains a direct path to performance while also working through credit-risk discipline, its effect reflects partial mediation. In contrast, the effect of AI operates entirely through credit-risk discipline, indicating full mediation. Regulatory pressure strengthens the influence of both ESG and AI on credit-risk discipline, demonstrating that stricter supervisory environments amplify the translation of sustainability and technological capabilities into more disciplined lending practices. These findings underscore CRD as the operational hinge through which sustainability and digital capabilities are converted into superior lending outcomes, highlighting the importance of governed data pipelines, explainable risk models, and effective early-warning mechanisms, supported by aligned managerial incentives and supervisory expectations. At the societal level, disciplined ESG- and AI-enabled lending reduces information frictions, supports equitable credit access for credible SMEs and households, stabilizes credit cycles, and mitigates adverse selection when paired with safeguards on privacy, transparency, and bias control. Overall, the study offers an integrated, theory-driven, institution-level assessment of how ESG and AI capabilities translate into measurable performance within a multi-country ASEAN context, clarifying when and how strategic and regulatory forces jointly improve credit outcomes.