Leaders Should Temper Expectations Around Gen AI In Finance: Gartner

While artificial intelligence in finance has generated significant interest from chief financial officers looking to maximise resources and improve efficiency and decision making, significant hype in the marketplace is likely to lead to a period of disillusionment with various technologies in this space, according to research and consulting firm Gartner Inc.

Gartner has released the Hype Cycle for Finance AI and Advanced Analytics, which shows the leading tech innovations in finance. It provides CFOs with the current state of key AI and advanced analytics techniques relevant to finance.

According to the Hype Cycle, generative AI is at the peak of inflated expectations in finance. As per Gartner experts, a range of publicly available gen AI tools have generated publicity for the technology in the last two years, but as finance functions adopt this technology, they may not find it as transformative as expected.

Although Gartner experts forecast disillusionment with gen AI tools in finance, the technology will still be useful for finance professionals. Gen AI uses text as its source, so for tasks that require analysis of text, such as contract analysis, it will have applications.

“Finance functions could also use gen AI to do things they currently don’t. For example, comparing an inbound vendor invoice with the negotiated pricing to make sure charges align with the agreed prices,” said Mark D. McDonald, senior director analyst in the Gartner finance practice.

“The main strengths of gen AI in finance are its ease of access and simplicity of use. With many vendors offering private in-house gen AI solutions, harnessing such tools is largely a case of teaching employees how to use it and under what circumstances it is a reliable solution,” added McDonald.

With regard to tasks that are based on numerical data, finance functions will need to rely on various applications of machine learning. This can help finance professionals with tasks like forecasting revenue, finding errors in large volumes of data, analysing financial results and detecting trends that otherwise could be missed.

“One of the main benefits of machine learning is that finance leaders can quantify the quality of the algorithm's output which can serve as evidence for auditable transactions,” said McDonald.

Composite AI refers to the combined application of different AI techniques to improve the efficiency of learning to broaden the level of knowledge representations. As AI adoption matures in finance functions, no single AI technique will be perfect for different functions, and combining AI techniques will be more effective than relying only on heuristics or a fully data-driven approach.

According to Gartner, organisations are being driven towards composite AI because appropriate actions can be better determined by combining rule-based and optimisation models, a combination often referred to as prescriptive analytics. Small datasets, or the limited availability of data, have also pushed organisations to combine multiple AI techniques.

A composite AI solution is composed of multiple agents, each representing an actor in the ecosystem. Combining these agents can enable the creation of common situation awareness, more global planning optimisation, responsive scheduling and process resilience, Gartner said.