Generative AI-Enabled Green Banking: An Empirical Study of Sustainable Finance and ESG Performance in Indian Banks

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DOI: https://doi.org/10.63345/ijrhs.net.v14.i9.1 

Najia

Research Scholar

Institute of Business Studies

Chaudhary Charan Singh University, Meerut

Reena Singh

Research Supervisor

Institute of Business Studies

Chaudhary Charan Singh University, Meerut

Abstract— Indian banks are simultaneously absorbing two structural shifts: a regulatory push toward climate-aligned finance following the Reserve Bank of India’s Framework for Acceptance of Green Deposits (2023) and SEBI’s BRSR Core assurance mandate, and the rapid diffusion of generative artificial intelligence (GenAI) into disclosure, credit-appraisal, and risk-monitoring workflows. Whether the second accelerates the first remains empirically untested in an emerging-market banking context. This study constructs a Generative AI Enablement Index (GAI-EI) from annual reports, BRSR filings, and investor communications of 32 listed Indian scheduled commercial banks over FY2018-19 to FY2024-25 (224 bank-year observations) and examines its association with composite ESG performance. Using two-way fixed-effects panel regression with Driscoll-Kraay standard errors, a bootstrapped mediation model, and a difference-in-differences design anchored on the FY2022-23 GenAI inflection, the study finds that a ten-point increase in GAI-EI is associated with a 2.14-point improvement in ESG score (β = 0.214, p < 0.01). Green finance intensity mediates 37.9 per cent of this effect, indicating that GenAI improves ESG outcomes substantially through the reallocation of credit rather than through disclosure quality alone. The effect is significantly stronger in private-sector banks. The findings position GenAI as a measurement-and-allocation infrastructure for green banking rather than a reporting convenience.

Keywords: Generative AI, green banking, ESG performance, sustainable finance, Indian banks, panel data, BRSR

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