Since OpenAI launched ChatGPT-4, the biggest names in tech as well as myriads of budding start-ups have strived to create marketable solutions powered by generative AI.
It didn’t take long for analysts to realize that financial services was the sweet spot for the AI generation. Banks, insurers and capital markets firms are far more complex than the average manufacturer or retailer, with a large proportion of processes that lend themselves to automation or augmentation (see table below) . They are also subject to more regulations, with a compliance burden that requires large amounts of data and manual effort. And then there’s the fact that financial services involves an awful lot of language tasks, which Generation AI can handle without breaking a sweat.
Financial services companies have of course been investing in AI for many years. Banks, in particular, have been aggressively recruiting technology and data specialists from universities. This has not only allowed them to develop their AI capabilities in areas such as fraud management; it has also created attractive research and working environments for academics and other rare specialists, thereby facilitating recruitment as well as partnerships with academia.
So what is the likely trajectory of the AI generation in financial services? I think we’ll see two main trends, working in different directions.
First, tech giants will continue to invest heavily in large and complex systems that address horizontal business functions in a generic but large-scale way. Finance, IT, sales and marketing, human resources and many others will all be deeply impacted. These systems will be expensive to build, train, and operate, so there will likely be relatively few contenders for market dominance.
In many cases, however, it will be difficult to audit, manage and govern these models to the standards that a financial services organization would require. In some ways, the AI generation in financial services is a bit like the steam engine when it first emerges. There was a lot of initial enthusiasm, but to take advantage of it, people needed faster ways to lay out railways and manufacture rolling stock, as well as create other important infrastructure. We must first lay the groundwork for financial services to fully exploit generative AI.
The second trend, I believe, will be the proliferation of highly specialized start-ups targeting not only specific sectors but also niches within each sector. These vertical systems will be smaller, simpler and cheaper, and enabled by advanced data segmentation and modeling. In financial services, they will handle a large portion of the 73% of banking tasks and 70% of insurance tasks that are ripe for automation or augmentation.
This verticalization and specialization of models creates opportunities for fintechs to capture particular niches and train their models on very specific data sets. This will enable disproportionate results. Some applications will boost existing tasks and processes; others will completely transform them or introduce services or capabilities we hadn’t yet thought of.
The attractiveness of this opportunity is reflected in the surge in venture capital funding, which has to some extent reversed the fall in fintech funding caused by rising interest rates. Meanwhile, many existing fintechs are launching and leveraging AI generation in specialized ways, benefiting financial service providers. According to Cambridge Center for Alternative Financearound 90% of all fintech companies already use some level of AI in their business models, all working simultaneously to carve out their respective niches in the market.
An example is How so, which helps businesses solve data availability issues by generating synthetic data that is private, fully auditable, and usable for any task. Auditing generation AI models and the data they use is highly evolved and extremely important for financial services stakeholders to ensure the absence of decision-making bias.
In wealth management, there is Responsive AI, a cutting-edge action platform that uses AI generation for document analysis and personalized email generation for advisors. Other examples include SkyHivea workforce reskilling solution that leverages AI generation to organize workplace data, automate HR processes, and drive a dynamic, skills-based labor economy, and Nuclius, which integrates AI search and generative responses into third-party products.
As FS companies need to manage explainability, privacy, and security risks, the adoption rate of Gen AI-based solutions could be significantly faster than any other industry as companies give prioritizing use cases, including software development And knowledge management chatbots to support reception staff. However, banks and insurers will need to be attentive to AI generation regulations as they develop and ensure they meet appropriate standards and guidelines across multiple geographies.
Adoption will be driven not only by rapid advances in technology in general, but, more importantly, by AI’s inherent ability to perpetually self-improve. Recent surveys have consistently shown that a large majority of business leaders recognize this and are reporting increased investment in technology.
It is too early to predict the exact impact of the AI generation on financial services, but it seems certain that there will be significant opportunities to increase personalization, improve relationship management and customer service, and improve efficiency through automation of language-intensive tasks. Fintech companies may well be at the forefront of this movement.