Since the release of ChatGPT last November, long-running debates around the broad use cases for AI – and the democratic, employment and social consequences of the technologies – have become far more heated. As the prospective applications proliferate, so has the fear and hyperbole. But what is the reality for the funds servicing industry?
AI, and its ability to mine big data, is transforming how investors and fund managers do business, noted Preqin First Close editor Shaun Beaney in the publication’s 19 June newsletter. “For fund administrators, placement agents, law firms, accountants, and investment bankers there will likely be new services, new roles, and therefore, new fee-earning potential. Some functions will be partially automated.”
AI-driven automation across the value chain
AI’s potential to drive automation across the transaction and client servicing value chain is a particular focus. With financial and operating pressures on fund administrators continuing to build, the ability to deliver better service more efficiently at lower cost and higher speed has obvious appeal.
The automation potential extends across multiple areas and takes numerous forms. Large language models, for example, can power digital agents able to field routine customer service queries. AI can capture and leverage the siloed data held in disparate parts of the organisation to inform and improve business processes. It can scour legal documents for relevant clauses in contract disputes, regulatory compliance cases or to automatically craft new agreements. It can translate documents from one language to another.
Such automation opportunities offer fund administrators significant – perhaps unexpected – benefits, notes AI-as-a-service provider Jaid. Integrating artificial intelligence tools into processes can:
- Help businesses run better by reducing the risk of human error in data input and customer interactions, and by providing more real-time support for client queries.
- Reduce risk by identifying areas where processes are letting clients down or creating regulatory risk, insights that can be used to streamline workflows and remediate problems.
- Tackle workload backlogs thanks to AI’s ability to quickly parse huge volumes of information and so address client queries faster, more comprehensively and reliably.
- Bolster staff morale by taking on mundane administrative tasks, freeing staff to focus on high-value activities that are more satisfying and keep them engaged.
The result, at least in theory, is more productive employees working on more value-adding tasks, better quality outputs, faster and more responsive services producing happier clients, and less regulatory risk.
Generative AI in transfer agency
Any task that follows a rule and has recurring patterns lends itself to AI analysis and production. By programming in many of the variables, AI can potentially take on much of the grunt work involved in knowledge economy jobs.
Transfer agency is a case in point. Many TA processes are data entry-heavy, prompting fears that AI will replace humans and make them redundant. In taking on the rote work though, AI gives employees the space to concentrate on the more creative elements of their roles, such as performance calculations and enhancing interactions with clients to improve the customer experience.
Eliminating human errors in trade inputting also reduces the compensation payments to clients from losses on investments stemming from those errors, helping offset the cost of implementing the new technologies. Plus AI offers a way to bridge the industry’s talent shortage gap and the impact that is having on firms’ business growth.
Artificial intelligence capabilities can play a similarly valuable role in anti-money laundering and know your customer (AML/KYC) tasks. The attention to detail and monitoring at speed and scale that AI technologies offer can be used, for example, in the continuous checking and refreshing of AML/KYC processes, and investigating screening “hits”. Human experience can then provide the judgement required to determine where additional action is required and how best to do it to minimise unwanted disruption to the investor journey.
Artificial intelligence still needs humans
As AI use cases develop, some roles, such as manual data input tasks, look set to disappear, or at least diminish. Whether that will result in job cuts, or simply staff being redirected towards other functions remains to be seen.
AIs do ‘hallucinate,’ producing plausible answers that are made up and completely wrong. As such, they cannot be relied upon fully yet. And where artificial intelligence goes wrong or creates unintended consequences, especially in highly-regulated industries like investment management, the financial and reputational risks can be enormous. Which is why humans – with their ability to critically reason – still have a vital governance and quality control role to play.
As Preqin First Close editor Beaney observed: “Expert professional judgement could become even more important in a world of AI and big data.”
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