@Indus Valley Partners.

This article was produced by Indus Valley Partners as part of their valued industry partnership with Fund Operator.

Generative artificial intelligence, also known as GenAI, is quickly coming into its own in finance. The buy side seems to have collectively overcome its initial scepticism of this new technology, and a consensus is emerging that GenAI will be fundamental in the industry for the foreseeable future. While it is true that adoption remains relatively slow, investments in GenAI are starting to increase.

In here, we will take a look at the current situation with GenAI on the buy side, explore the potential
opportunities and benefits of these models, and then look at why these efforts will only be successful with appropriate preparation and planning.

WHY GEN AI IS GAINING MOMENTUM?

GenAI is becoming more popular for a very simple reason. Buy-side firms have started to recognise the value these models can bring to both liquid and illiquid markets.

For liquid assets, firms are using GenAI to generate front and middle office intelligence and find alpha as well as back-testing GenAI models to become more predictive. These use cases can be adopted faster because there is more data available to train the models.

For illiquid or esoteric assets, GenAI is more focused on operational optimisation to support faster and better decision-making. These use cases are slower to adopt because training data is more difficult to capture.

The key point here is that acquiring an adequate amount of training data is a non-negotiable part of the journey toward successful GenAI adoption.

HOW GEN AI CAN HELP THE BUY SIDE COMPETE?

Before we get into planning and preparation, let’s touch briefly on the benefits that GenAI can bring to asset managers. All of these advantages have the capacity to give firms a competitive edge and drive growth.

FASTER INSIGHTS

Discovering new insights from the same data is one way buy-side firms build narratives and investment strategies. With GenAI, intelligent models can be trained to help firms stay aware of trends and other factors and support fundamental analysis. This makes it easier to see how the market is behaving in response to certain events and driving investment prices accordingly.

BETTER TRANSPARENCY

GenAI models can also be used to improve transparency, in this case the transparency of available data required for various tasks. With on-demand access to the right data for any need, teams across the enterprise can improve the speed and agility of
analysis, hypothesis development and testing. Again, this helps asset managers act on new strategies more quickly and effectively.

OPERATIONAL EFFICIENCY

GenAI models can be trained to automate and accelerate many of the workflows funds rely on every day. In private funds, these might include collecting KPIs, creating balance sheets, preparing income statements, and retrieving other datasets. Overall, the emphasis is on improving automation to minimise manual workflows. GenAI will also play a role in improving data quality by identifying discrepancies or anomalies and resolving them faster to optimise decision-making.

BUILD THE FOUNDATION FOR GENAI

It should be clear that where GenAI can make the biggest impact is by helping buy-side firms get the
right data in the right place more quickly and accurately. But this can’t happen unless firms already have the proper data infrastructure established on an enterprise-wide scale.

Specifically, the prerequisites for effective GenAI adoption include:

• Data democratisation: Data is available to access outside of conventional silos
• Master data management: Versatile data handling processes that improve operational agility
• Single version of the truth: All teams work from the same reliable, fully harmonised data
• Governance frameworks: Data handling is guided by standard protocols to promote compliance
•  Transparency: Changes and updates to data sets are trackable for easy auditability
• Automation: Data workflows rely on automated processes rather than manual intervention
• Connectivity: All systems that use data are seamlessly integrated across the enterprise 

WHY A STRONG FOUNDATION IS CRUCIAL

If all of this sounds like a tall order, you are correct. But without all of these capabilities—otherwise known as a state of “data maturity”—GenAI adoption will struggle. Here’s why:

GenAI is very much a “garbage in, garbage out” technology. Without adequate, relevant training data, models will simply not be able to accomplish the tasks that buy-side firms want them to. Lack of broad connectivity to data substrates (like the Snowflake platform) will prevent analysis of document libraries and overall data harmonisation. Furthermore, data will not be structured or arranged so that GenAI can make use of it for training models.

Here is another way to think about it. Allowing GenAI to access search, document processing, anomaly detection, data quality management, and other points of access is only one part of the story. If the data itself is not properly prepared, GenAI models will not return any valuable insights. Which means firms that have not yet achieved data maturity will be wasting investments in GenAI.

By ensuring the building blocks for GenAI are in place, firms can move quickly from concept to training the model, and from training to execution.

CONSIDER THE IVP MDM SUITE

Buy-side firms that want to jump-start the journey to data maturity should consider the IVP Master Data Management Suite. This integrated collection of cloud-enabled solutions are designed to organise, categorise, and localise diverse data sets with built-in flexibility, transparency, and governance. Importantly, all of the components in the suite integrate seamlessly, enabling firms to improve data quality and use it more easily, efficiently, and economically across the enterprise.

To learn more about the IVP Master Data Management Suite and preparing your firm for successful GenAI adoption, talk to an IVP expert in data management.

 

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