Regulatory AI as Catalyst: Framework for Sustainable Financial Transformation

Röser, Alexander Maximilian and Bartelt, Cedric and Weiß, Ricky (2026) Regulatory AI as Catalyst: Framework for Sustainable Financial Transformation. In: FEJLŐDÉSI PÁLYÁK ÉS ÚJ TÖRÉSVONALAK A FENNTARTHATÓSÁGI ÁTMENET IDŐSZAKÁBAN : Nemzetközi tudományos konferencia a Magyar Tudomány Ünnepe alkalmából. Soproni Egyetem Kiadó, Sopron, pp. 678-694. ISBN 9789633345795

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Abstract

The growing dynamics of regulatory change in the financial sector demands adaptive and future-proof internal processes to safeguard banks against process and compliance risks. This paper presents a modular, conceptual Regulatory AI framework that acts as a catalyst to strengthen corporate governance and improve the resilience in digital financial transformation. Leveraging Natural Language Processing (NLP), Machine Learning (ML), and generative AI (GenAI), the framework screens regulatory sources in real-time, identifies relevant changes based on their impact on internal workflows (e.g., risk management, reporting, and compliance) and seamlessly integrates them into existing systems. It consists of four core components: Data Ingestion, Screening and Analysis, Process Integration, and Continuous Monitoring. By embedding principles of sustainable finance and responsible AI governance, the framework not only enhances regulatory responsiveness but also supports the alignment of financial institutions with long-term ESG objectives. Its implementation of the framework can reduce manual efforts and enhances process resilience. The approach promotes proactive adaptations regarding regulations such as the EBA (European Banking Authority) Guidelines or MaRisk (Minimum Requirements for Risk Management). Drawing on empirical implications from current developments, the framework provides a strategic foundation to link digital transformation with long-term operability.

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