At the recent AFME OPTIC conference in London, a panel of industry professionals from Nomura, BNY, and Xceptor discussed these topics in detail. They talked about how financial data can change lives and compared it to "gold" when used properly.

Even with the abundance of data available today, Corlytics claims that there are still major challenges when it comes to granular, unstructured, or fragmented data that is dispersed among different organizational silos. The rising relevance of AI-driven systems in trading and decision-making underscores the need of embracing these underutilized data sources.

The significance of dark data—unstructured, unused, and frequently disregarded data—was emphasized by Ling Ling Lo, global head of data strategy and transformation and chief data officer for EMEA at Nomura.

Even while it might be difficult to get and handle, this kind of data is essential for creating AI and trading models of the future. Because of legal obligations, traditional data, such trade and customer information, is easily accessible. However, because dark data is unstructured and may be discovered in emails and documents, it is still mostly unexplored and presents substantial hurdles for analysis using AI.

Institutions must refocus their attention from structured to more complex, unstructured data sources in order to properly utilize AI in financial services. With the use of increasingly advanced predictive algorithms, this strategy may significantly improve AI-driven operations and provide banks with a major competitive edge.

The significance of AI and machine learning in improving business outcomes through sophisticated data use was emphasized by Archie Jones, SVP of data and AI ethics at BNY. The financial markets, where granularity and real-time analysis are crucial, might undergo a transformation because to generative AI's capacity to transform unstructured data into useful insights.

There was also discussion of AI's function in data process automation. AI's capacity to analyze emotion in communications allows businesses to glean insightful information from routine exchanges. The efficiency with which generative AI can summarize and analyze complicated texts is especially noteworthy, since it has the potential to save a substantial amount of time and money.

But this progress is not without risk: inaccurate predictions made by AI, or "hallucinations" brought on by shaky data foundations. Chief technology officer of Xceptor Dan Reid stressed the value of Retrieval Augmented Generation (RAG), which enables AI models to extract information from particular, confidential documents, guaranteeing more dependable results.

One noteworthy trend is the financial services industry's radical transition toward cloud use. The shift to cloud platforms has been transformative, especially in terms of solving the problems associated with legacy data. Cloud solutions offer the scalability and flexibility needed to improve data models and turn historical data into an asset worth valuing instead of just a financial burden.

The CTO of Xceptor noted that quick access to data is essential for engineers, and cloud technologies make this possible by greatly accelerating the processes of data retrieval and usage.

The future of financial services lies in AI and data trust. Ling Ling Lo emphasized that in order for businesses to develop their models efficiently, they must gather high-quality data and use massive language models to convert unstructured data into formats that are structured. Learning data management in the financial markets is still an essential, continuous process as we go further into the AI era.