When it comes to AI in financial services, we can divide things into two generations:
- The present (classical AI)
- The future (generative and agentic AI)
Today, classical AI is widely used across the industry. In fact, banks have been using it for decades now. In the early days, they called it by different names, including “machine learning” and “business analytics.” Over the years, they’ve gotten very good at using predictive models for use cases like fraud detection and risk modelling.
As the next generation of GPUs are deployed, classical AI will evolve fast, and models will be able to process much larger datasets much faster. This means that banks will be able to perform Monte Carlo simulations, common for predictive modelling, on a greater scale to provide a clearer, faster picture of risk.
The next generation of AI in financial services will involve large language models (LLMs) and agentic AI workflows. In some cases, this means firms will evolve their products and services to be better and faster, with the aim of improving customer satisfaction and increasing revenue and profitability. Embedding LLMs into the customer-facing side of the bank is inevitable and one of the most exciting possibilities for AI.
Three emerging AI use cases for the financial services industry
In addition to evolving existing use cases, advanced AI technology will unlock use cases that are entirely new. It’s not a question of whether firms will adopt these use cases; it’s only a matter of when and how they’ll do it. Let’s take a quick look at a few of these use cases, and what firms need to do to prepare.
Algorithmic trading
High-frequency trading (HFT) firms can supplement their existing processes with AI models that help them get ahead of market trends. This could include pairing traditional financial data with data from alternative sources. For instance, data pulled from social media networks could provide insights into consumer trends before those trends start to show up in the market. Thus, HFT firms can put themselves in a better position to execute trades quickly and capitalize on opportunities the very moment they appear.
Customer engagement
With generative AI, banks can provide a human interface that enables better, more personalized customer service. This could include collecting information about a particular customer and then suggesting customized financial planning for that customer. Chatbots could also improve the user experience by helping customers quickly gather the information they need to make informed decisions.
Operational efficiency
It’s an old business adage that anything that can be automated, will be automated. The financial services industry is no exception to this rule. Banks still perform many manual processes today. Automating them could help drive greater efficiency and customer satisfaction.
This is where agentic AI could be especially helpful. Different AI agents can perform individual tasks in order to achieve a common goal. For instance, if the goal is to respond quickly to loan applications, then one agent might take in data from the applications, while another performs compliance checks and a third helps suggest the loan rate.
What’s stopping firms from adopting advanced AI?
In some ways, identifying use cases is the easy part. In fact, most firms are already testing these use cases internally. However, there’s a big difference between testing AI models and moving them into production.
For one thing, firms need to have complete confidence in the accuracy of their models, and that alone is a tall order. Also, the industry is heavily regulated, which means models need to be both accurate and compliant. Even if a firm creates a model that meets both criteria, it would still need to implement that model into its business workflows, which is not as simple as flipping a switch.
Let’s consider banks planning to use chatbots in their loan approvals process. First, they need to clearly articulate the business value they expect to gain from doing so; it can’t just be a matter of buying into the hype. Then, they need transparency into why their chatbots return the results they do. This includes ensuring that there’s no hidden bias impacting the results. Finally, they’ll need to change the way they lend money to incorporate the new automated processes, which could involve completely rewiring a massive global operation.
It’s easy to see why firms are taking a cautious, measured approach to advanced AI use cases. This transformation will take years to fully play out.
How banks and trading firms can start getting AI-ready
Despite the challenges ahead, there are steps that firms can take today to ensure they’re ready to implement advanced AI use cases when the time comes. Like everything else in AI, it all comes back to the data. Companies need to know they have the right data from the right sources at the right time. Perhaps most importantly, their future-proof AI data strategy needs to define where the data will be stored. It’s essential to get this right today, because in the future, production AI models will need to be deployed in proximity to the data.
Today, most financial firms use public cloud environments to host their AI testing. This may have seemed like the logical choice at the time: The cloud was a quick and convenient way to access the resources they needed for their AI models. However, when it comes time to transition from testing to production, the public cloud may no longer be the best option.
On the other hand, building production AI in their own data centers may not be the answer either. Many firms’ owned-and-operated data centers have constraints around space and power availability—something the power-hungry GPUs may test. Building inside on-premises data centers also locks firms into a certain way of doing things. When the need inevitably arises to move models and data, doing so will be difficult and expensive.
Firms will need to deploy their production AI close to all their data sources, including their mainframes, their trading data and their payments and fraud data. This need for proximity will drive many of the decisions on where the data needs to reside. As they place their current datasets, firms need to consider the space and power requirements of any AI GPUs they might deploy in the future.
It’s clear that public cloud and on-premises data centers aren’t appropriate for all scenarios. So then, what option does that leave for financial firms looking to get AI-ready? Deploying inside a high-performance colocation data center could be the answer.
Colocation at Equinix enables an AI-ready data strategy
Leading colocation providers like Equinix are always investing in AI-ready technology, including GPU hardware and the advanced cooling capabilities needed to keep them running. Also, Equinix is the global leader in cloud on-ramps. By deploying in proximity to the clouds of their choice, our customers can integrate cloud services into their AI strategy, without having to give up control over their data.
Colocation at Equinix can also simplify the process of implementing a private AI strategy. Private AI is when a firm develops its own models, uses proprietary data for training and inference, and hosts everything on dedicated infrastructure. This is in contrast to strategies that incorporate public LLMs, trained on data from publicly available sources and hosted in multitenant environments.
With colocation at Equinix, you can ensure proximity between models and data, on-premises data centers and your public clouds. This ensures the low latency required for advanced use cases. You can also keep your data where it needs to be in order to meet sovereignty requirements.
As financial firms begin to transition AI workloads from testing to production, the control that private AI offers will become more important than ever. Learn more about Equinix Private AI with NVIDIA DGX, our turnkey, ready-to-run AI development platform, and how it can be especially helpful in heavily regulated industries like financial services.
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