Research on the Transformation Acceleration of Financial Institutions and Governance Efficiency with Artificial Intelligence Technology

This study aims to evaluate the impact of artificial intelligence (AI) integration on the performance and governance efficiency of financial institutions. To address potential endogeneity concerns arising from reverse causality and omitted variable bias, we employ System Generalized Method of Moments (System GMM) estimator, complemented by Fixed Effects (FE) and Random Effects (RE) models for robustness checks. Our findings indicate that AI integration significantly enhances return on assets (ROA), operational efficiency, risk-adjusted returns, and customer satisfaction while reducing compliance costs and regulatory breaches. However, challenges such as algorithmic bias and workforce displacement must be addressed. In conclusion, AI offers substantial benefits to financial institutions, but ethical considerations and strategic workforce planning are essential for sustainable integration. These insights provide valuable guidance for financial institutions and policymakers aiming to harness AI's potential while mitigating associated risks.

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Publication Details

Journal
Tehnicki vjesnik - Technical Gazette
Published
2026-05-01
DOI
https://doi.org/10.17559/tv-20251226003240
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
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article

Research on the Transformation Acceleration of Financial Institutions and Governance Efficiency with Artificial Intelligence Technology

Keying Li, Huiping Shang, Jingzhi Han, Guangyuan Han
Tehnicki vjesnik - Technical Gazette
FinTech, Crowdfunding, Digital Finance
article

Research on the Transformation Acceleration of Financial Institutions and Governance Efficiency with Artificial Intelligence Technology

Keying Li, Huiping Shang, Jingzhi Han, Guangyuan Han
article en

Abstract

This study aims to evaluate the impact of artificial intelligence (AI) integration on the performance and governance efficiency of financial institutions. To address potential endogeneity concerns arising from reverse causality and omitted variable bias, we employ System Generalized Method of Moments (System GMM) estimator, complemented by Fixed Effects (FE) and Random Effects (RE) models for robustness checks. Our findings indicate that AI integration significantly enhances return on assets (ROA), operational efficiency, risk-adjusted returns, and customer satisfaction while reducing compliance costs and regulatory breaches. However, challenges such as algorithmic bias and workforce displacement must be addressed. In conclusion, AI offers substantial benefits to financial institutions, but ethical considerations and strategic workforce planning are essential for sustainable integration. These insights provide valuable guidance for financial institutions and policymakers aiming to harness AI's potential while mitigating associated risks.

Tehnicki vjesnik - Technical GazetteVol. 33(3)
Nanjing Normal University (CN), Puyang Vocational and Technical College (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 8%
FinTech, Crowdfunding, Digital Finance
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