Artificial intelligence is being adopted by financial regulators at pace, putting pressure on the financial institutions that the overseers serve to double down on their reporting capabilities.
It’s no surprise to find that the same AI that’s helping regulators can aid organisations in getting those reporting procedures in place. To do so, however, they need to have their AI data in a fit state for absorption and use by the powerful models that form the backbone of the technology. Without that, argues a new white paper by Snowflake and Amazon Web Services, institutions may be left vulnerable to compliance log jams and potential penalties.
The best way to achieve that, the paper states, is through cloud-based data management, which offers institutions the computational and analytical power they need at scale.
“Data is only of value if it can be accessed and used with ease,” the report states. “By hosting data on the cloud, enterprises break down data storage silos that hinder its accessibility. It also shortens the data pipeline from source to user, reducing friction and permitting the safe hosting of a golden data source that ensures all processes work from the same script.
“A single source of truth ensures that all users within an organisation have access to the most updated iteration of that data, enabling seamless collaboration across teams and departments,” the paper, published by A-Team Group, adds.
Growth Projections
AI is taking the world by storm, and no less so in the financial services industry. A recent International Monetary Fund report found that institutional spending on AI was forecast to surge 29% to US$97 billion by 2027. Snowflake’s own technology experts predict that generative AI (GenAI) will become a “make-or-break” technology for institutions.
Snowflake research also highlights the growing adoption of AI. One study found that usage of Python, a popular language for AI development, surged 571% in 2023 among all industries, much more than any other language. Within financial services the leap was even greater, at 585%.
Among the many imperatives to integrate AI effectively into organisations’ systems is the rapid adoption of the technology by regulators, who “have seen the efficiency gains and cost savings that AI-led automation can bring”, the paper, entitled “Unleashing the Power of AI for Financial Institutions with Snowflake and Amazon Web Services”, states.
Because disclosures by regulated companies require the harvesting of information from all parts of their enterprises, the burden can be onerous, especially if the information is locked within a fragmented tech stack. However, with AI able to query a vast range of variables to help it alight on the optimum output, it’s crucial that the technology has easy access to clean, accurate and standardised data.
Ageing Setups
Legacy infrastructures are often incapable of offering the necessary foundational bedrock for that. They often can’t fully absorb and respond in a timely and compliant manner to the rapid changes to regulatory codes around the world. Worse still, aged setups can’t manage modern automation technology that can do the preparatory work that is essential to complying with oversight rules.
Other use cases that need an efficient data strategy to unlock the optimal application of AI include data gathering processes, analytical modelling, productivity improvements as well as risk and return management, the paper states.
With organisations facing data management-related hindrances to AI integration at a time when the need for the technology is rising, the paper argues that cloud-based data management by a trusted partners, such as Snowflake and Amazon Web Services, will provide the necessary capabilities.
“The near-limitless capacity of a cloud environment, such as AWS, to host computational tools and store raw data provides the optimal environment for AI to be applied to organisations’ use cases,” the paper argues.
It goes onto state that this is particularly important for regulatory reporting teams who “must work across multiple departments and engage with external regulatory counterparts. For most institutions, this is a multi-jurisdictional obligation.
“All this calls for efficient and extensive collaborative capabilities” that data management in the cloud can offer.
“Putting a robust data strategy in place calls for deep expertise and experience. It takes patience and planning. It argues for partnership with an expert that already provides the digital backbone and knowhow that supports the cloud-based data setups of scores of leading institutions.”
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