In a world that is constantly digitally evolving, AI is proving to have the potential to be one of the biggest change-makers. How is it shaping finance?
In a recent post by Theta Lake, the company provided a greater outline of how artificial intelligence is changing the sector.
Artificial Intelligence (AI) has garnered significant attention lately, with ChatGPT, its generative iteration, being at the heart of the conversation. This development has elicited a spectrum of responses, ranging from apocalyptic predictions to optimistic visions of societal transformation. Financial service firms are situated at this intersection, striving to harness the true potential of AI while navigating through the competitive landscape.
In today’s digital world, the volume of communications data being produced outstrips the manual review capabilities of both regulators and financial services firms. The adoption of dynamic unified collaboration (UC) tools such as RingCentral, Zoom, Cisco Webex, Slack, and Microsoft Teams has accelerated this trend. Consequently, organisations are increasingly turning to AI technologies to manage this data deluge. AI empowers firms to analyse vast volumes of communications data, detect risks and breaches at scale, issue timely alerts, and streamline review priorities.
The integration of AI into financial services, however, requires adherence to a few cardinal rules. The first of these is understanding that irrespective of the medium of communication – be it AI-generated or handwritten – the accountability for the compliance of the content rests with the firm. Robust and comprehensive recordkeeping is another prerequisite for leveraging AI in monitoring communications. Firms must preserve an expanding array of content, including emojis, GIFs, and chats, and ensure the ability to retrieve it in its native context.
Given the dynamic nature of risk management in the financial sector, a human-in-the-loop approach is necessary to guide the AI and ensure accountability. The implementation of AI-based risk detection models, which are pre-trained to focus on specific conduct, compliance, or security risks, has several potential benefits. These models can be trained to identify sensitive data like account numbers, email addresses, birthdates, customer lists or applications such as trading screens, HR or finance systems.
Crucially, a well-trained AI’s capability to understand specific risks in context can minimise the occurrence of false positives and detect risks that might be overlooked due to unclear audio or transcript. The selection of high-quality expert sources and domain expertise for training an AI model remains a human-dependent process, reinforcing that firms cannot solely rely on generative AI like ChatGPT for judgement calls and experience required to train a specialised AI model.
With proper data retention and the deployment of domain-specific AI, the financial services sector stands to gain significantly from AI integration. This technology promises to enable firms and regulators to quickly identify risks, while delivering substantial cost savings and operational efficiencies.
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