In the realm of technology and business, 2023 will go down in history as the “the year of generative AI.” While it initially garnered attention for its creative applications in content and image generation, the true potential of generative AI lies in its ability to unlock new ways of thinking and enhance efficiency in the business world. This transformation is set to have a lasting impact for decades to come.
According to a Goldman Sachs Research report published earlier this year, generative AI is reshaping business workflows, promising a 1.5% boost in global productivity. This efficiency gain, where decisions in financial services are made in real-time and on a second-by-second basis, can be a game-changer and allow professionals to redirect their time toward higher-value tasks.
However, the financial sector, with its stringent regulatory oversight, will be closely watched as generative AI adoption accelerates in the coming year. As we step into the new year, here are the key trends and factors that will shape the conversation around generative AI in investment accounting and the broader financial services sector.
Streamlining client onboarding and compliance
Client onboarding is a time-consuming process in the financial services industry. In investment accounting in particular, this can take months to a year, if not longer, to onboard a client’s data, meet their bespoke technology integration requirements, and build the necessary foundation that is required for them to remain compliant with the various local, national and international oversight requirements placed on their portfolios. But what if it was possible to cut this time down in half? Generative AI can make this a reality.
Consider onboarding investment policy statements, typically ranging from 50 to hundreds of pages, filled with complexity crucial for regulatory compliance. Because guidelines often shift, investment accountants are routinely tasked with updating these investment frameworks to ensure compliance. To familiarize and synthesize these documents takes investment accountants weeks — if not months.
With generative AI, these documents can be quickly ingested into the software platform, enabling users to easily extract information on the most obscure key rules and regulations, such as the amount of an investor’s portfolio that can be dedicated to technology stocks in a local government, for example. Investment accountants can then confirm this information during onboarding with client compliance teams within minutes, and then quickly notify clients of where potential compliance issues may arise in the future.
Perfecting “prompt engineering”
Generative AI’s capacity to learn and adapt is truly impressive, but its effectiveness depends on the quality of prompts — something many people are still learning best practices for. In investment accounting, professionals and clients need answers to niche, specific questions, ranging from real estate investment trusts to exposure to the British pound. Therefore, without precise “prompt engineering” – or using hyper-specific and contextualized prompts — investment accountants may waste time searching for information.
Generative AI as a technology needs to be provided with nuance and context. In order to extract the required insights, prompts need to be as specific as possible. Moreover, using slightly different prompts for similar queries may yield different results. In investment accounting, time-to-insights is the name of the game, and therefore, prompt automation and templating are pivotal in enhancing generative AI’s efficiency for investment accountants in 2024.
Prioritizing transparency and auditability
Reviewing important policies and generating reports in investment accounting demands a high-level of transparency and auditability. Given the highly regulated nature of the entire financial services sector, generative AI responses need to get it right. Inaccurate responses have caused many financial organizations to take a cautious approach to generative AI adoption. At the same time, technologists are redoubling their efforts to provide “glass box” transparency and explainability in their generative AI responses to meet compliance standards — and quickly.
Not only do clients and regulators insist that decisions be easily explainable, but they also demand that both the decisions and decision-making processes behind them be clear and verifiable. Generative AI tools lacking transparency and safeguards against insight fabrication pose risks to investment accountants. Ensuring human operators are in the loop to review insights, detect anomalies and provide a clear view of decision-making processes is crucial for mitigating these risks. Transparency and auditability will continue to be hot topics in generative AI conversations among both Fintech companies and financial services end users in the year ahead.
The next big thing
If generative AI is successfully adopted, it has the potential to transform the financial services industry. This technology will introduce new methods to enhance efficiency and address longstanding challenges in investment management. By using generative AI responsibly and transparently, we can make notable improvements in a sector that has faced many challenges. By establishing new AI guardrails, I expect to see some very real, tangible business impacts result from this transformation.
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