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23rd January 2025 | Insights

Alpha generator: AI in reference data

Artificial intelligence pitches are fast becoming a regular feature of budget allocators’ briefs. While there is undoubtedly much hype around this new technology, it is clearly also a development with potentially revolutionary implications for the financial industry. One of the less explored corners of this trend is reference data, where early applications of AI are forming part of a step-change in how firms approach these data sets. 

In a recent Acuiti report, published in partnership with SmartStream, more than half of the 81 firms surveyed said that they were already using AI in their reference data management or planning to deploy it. This comes at the same time as many reassess how they use data sets that have predominantly been viewed as a post-trade consideration.  

While still in its early days, the use of reference data to maximise alpha generation is gaining momentum in financial markets. Traditionally an input for compliance and operational efficiency, a small but growing number of firms are using it for functions such as TCA and trade execution optimisation.  

This trend is still in its early stages, with only 13% of survey respondents extensively using reference data for non-post trade functions. However, given the potential of AI and the continuous nature of investment in reference data, momentum is likely to continue to build.  

Indeed, much progress has been made in recent years on improving efficiency in the traditional applications of reference data. Firms’ focus has centred on automating manual processes and harmonising data flows between systems.  

Where investment dollars have been targeted has varied according to firm type, however, with the buy-side and sell-side pursuing different priorities in their investments in reference data. Hedge funds and proprietary trading firms have focused on harmonising external data sources to enhance trade selection and back-testing capabilities. The sell-side has prioritised internal data harmonisation to support real-time risk management and improve straight-through processing (STP).  

These are also areas where AI can be expected to make a difference. Despite significant advances in automation, there are still challenges in fully automating the post-trade reconciliation process. Nearly half of survey respondents have mostly automated reconciliation processes, and 24% report full automation. However, challenges still remain in the bid to bring full efficiency to this function.  

AI models that reduce reconciliation discrepancies over time, without human intervention, will bring down operational friction, improve data quality and cut costs for firms. This also holds exciting potential for areas such as trade settlement and derivatives contract updates.  

AI is redefining how financial institutions manage and extract value from reference data. As the technology matures, investments in high-quality data, advanced reconciliation tools, and AI-driven analytics will separate leaders from laggards. The potential for reference data to move from a cost centre to a revenue driver is no longer speculative, it’s becoming a reality. 

To download the full report, visit: https://www.acuiti.io/the-future-of-reference-data-from-compliance-to-alpha/