How is AI being adopted, deployed, and advanced in firms across the financial services industry? It’s a question whose answer interests everyone from business leaders to IT professionals and data executives — at banks and in firms across several other sectors.
In partnership with Mastercard and Royal Bank of Canada, the MIT Technology Review has published a report with Dataiku exploring how AI is gaining momentum across financial services. In this blog, we’ll highlight the three key takeaways from the report and discuss what they might mean for the future of AI in banking.
Empowering Business Operations
“As AI continues to grow in financial services, AI-powered functionality will increasingly flow into the hands of employees and business operations. Working cooperatively with AI creates the most value, as it takes on manual, repetitive tasks that frustrate employees.”
This first takeaway speaks to the growing interest, across many domains of the economy, in data democratization. Where many companies have, for a long time, experienced the inconveniences of data silos within their organizations — that is, a communication barrier between the models and projects being developed by data teams and the decision-making processes being designed by business leaders — the new data landscape is a lot more transparent and collaborative. As financial services firms look to apply AI as widely as possible, from risk mitigation and compliance auditing to algorithmic trading and asset allocation, more employees and line-of-business experts are being empowered to understand and make use of models that previously only fell under the purview of data teams.
And AI can also help these users focus on the most important tasks at hand, minimizing wasted effort on tedious operations. As Masood Ali, senior director of data strategy and governance at RBC, puts it, “AI systems can remove the repetitive tasks that frustrate many employees—often since their efforts would be better leveraged elsewhere, especially with the latest capabilities to search intelligently through documents, spreadsheets, and other unstructured data.”
AI Education for Better Outcomes
“With education and increased exposure to AI, employees can find more job satisfaction, create better opportunities for customers, and build expertise, raising their value. Most companies don’t offer enough education about the best uses of AI.”
Despite much talk about how employees in all sectors of the economy are concerned over the security of their jobs with the rise of more sophisticated AI technologies, the truth is much more complex. In fact, according to a Salesforce survey, the majority of workers facing the prospect of working with AI are excited about and/or looking forward to it. The hitch is simply that they don’t quite understand it (“only 1 in 10 say their day-to-day work involves AI skills,” according to MIT). In other words, the problem is not so much one of will but of learning.
The report underscores the importance of exposing employees to AI systems and their applications — including large language models (LLMs) — for preparing a harmonious, symbiotic relationship between “man and machine” in the workplace. At the moment, not enough banks provide this kind of expectation, but most are well-equipped to do so.
Streamlined ESG Compliance
“Increasingly sophisticated AI platforms and better data will streamline the handling of regulatory requirements and ESG compliance. Ensuring data quality and model consistency will be important, as will removing biases from data.”
Meeting environmental, social, and governance (ESG) targets is essential for firms across the financial services industries. And yet it remains a difficult task with its fair share of teeth-pulling, in many cases. This is because “ESG compliance involves significant record-keeping and compliance requirements, often based on unstructured data as well as data from sources that are historically new for financial firms to absorb,” in the words of John McCambridge, global solutions director for financial services and insurance at AI and machine learning firm Dataiku.
AI systems can help structure, clean, and model data, saving data teams and analysts alike hours of tedious work per reporting period. As data teams adopt advanced analytics tools to help them embed ESG compliance across their processes, they find themselves meeting their targets more quickly, more reliably, and more efficiently.
The Future of AI in Banking
Among the many insights from MIT Technology Review’s report on the state of AI in banking, it’s clear that the three key takeaways outlined above represent three parts of a dynamic whole. In other words, growth in one of the three will almost certainly mean growth in the other two. As advanced analytics becomes less siloed within firms, democratizing access and even model development beyond data teams, analysts and line-of-business specialists will become better educated in the potential and possible applications of AI for their projects. This, in turn, will help the many teams within a given bank work in lockstep to meet essential business targets — ESG foremost among them.
But that’s only the tip of the iceberg. For a more robust view of the many ways in which AI is gaining momentum in banking — including essential perspectives on the topic from Mastercard and RBC — download and read the full report for yourself.