AI-Driven Innovations in Banking: Enhancing Risk Compliance through Advanced Data Engineering
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Abstract
As more and more financial services make use of artificial intelligence (AI), AI decisions are starting to impact matters that people care about—money. Across many areas, AI is now being trusted to meet regulatory compliance for high-quality training data, create user-friendly transparent requirements, and manage risk. As regulators take the time to better understand how to regulate AI technologies in tandem with engaging with AI innovators from the financial sector, new risks must be considered that AI technologies could bring to the financial services and what actions could be taken to mitigate them.
AI systems have found a wide range of application areas in financial services but their involvement in high-stakes decisions has escalated the need for compliance and effective model governance. The unique characteristics of AI, along with the governance practices that followed, have led to fundamental tensions that lie at the heart of challenges in AI model governance. Furthermore, the growth in model complexity raises questions on the sustainability of practices that are already under strain. Governance with regard to AI is still very much in its infancy, with regulators gradually catching up to the innovative development in AI. Banking sector struggles with issues such as deciding if tailor-made models to customer needs and newly created products are fundamentally sound and have submitted prevention measures to control their risks and the maintenance of model-derived decisions after deployment.
The governance of complex AI systems lends itself to a systemic view that considers the multiplication of models, sources, systems, teams across locations and regulators. It is key to also embed tackling mixed-nationality jurisdiction challenges into future regulations of algorithms in the banking sector as it is one of the key current matters discussed by authorities [2]. It would be best to take all socio-political pressures immediately into account to develop a comprehensive AI regulation framework for the deployment stage that takes into consideration and finds a balance across its risks while encouraging its deployment to spur the efficiency, cost and quality of products and services, risk-averse behaviours of customers and therefore the stability of the banking sector as a whole.
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References
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