Discover how some financial services firms are currently leveraging artificial intelligence and machine learning in fintech and regtech.
At the NSCP National Conference in November, I’ll be speaking on a panel with Jane Stabile and Andrew Siegel on the topic “Will AI Revolutionize the Financial Services Industry?” This is the second in a series of related blog posts on AI and its impact on the industry. Part 1 covered the current state of AI development in fintech. Here in Part 2, let’s look at how financial services firms are currently leveraging AI in their daily operations.
A couple years ago, Deloitte’s Insights series published an article announcing “the AI leg of the digital marathon.” The article noted that financial services firms on the cutting edge were already starting to implement “intelligent solutions such as advanced analytics, process automation, robo advisors, and self-learning programs.” But the article also recognized, at that time, we were a long way from widespread use of these technologies. Fast-forward two years, and we find that the deployment of AI in financial services firms is still in the early stages.
Where Are We Now?
EY recently published an infographic showing that most firms across all industries are still using “standard” tools to manage their businesses. For example, 95 percent of business owners still use spreadsheets or similar tools to manage their internal financial planning and analysis, while 57 percent use some form of business intelligence. “Emerging” tools include data visualization, reporting automation, cloud services, mobile applications, robotic process automation (RPA), and AI/machine learning. These last two tools, which will become the most disruptive in the industry, are used by fewer than 5 percent of firms.
So what’s the holdup? Part of the issue is data. Building AI/machine learning tools requires large data sets to train and refine the underlying machine models. Again, EY points out that “there has been a minimal shift in the time allocated to gathering data.”
Another reason for the slow spread of AI in the financial services industry is cost. Two years ago, 87 percent of Deloitte’s frontrunners in AI initiatives were spending over $500,000 per year on AI development, while a whopping 45 percent were spending over $5 million per year. The time, money, and resources needed to develop functional machine learning algorithms are substantial.
For example, IMP developed its CLEAR Compliance system using a type of machine learning commonly referred to as natural language processing, or NLP. IMP’s CLEAR Compliance system helps asset managers facilitate critical components of their investment trading compliance program by automatically reading prospectuses and identifying trading rules and restrictions. We asked Jon Gold, Managing Director at IMP, to tell us more about his experience with machine learning in fintech and regtech.
Read our conversation with Jon Gold.
Where Are We Going?
While consulting firms like IMP are using machine learning to develop specific solutions for financial services firms, other fintech companies are taking an aggregator approach. For example, digital banking unicorn Revolut offers bank accounts, debit cards, fee-free currency exchange, stock trading, cryptocurrency exchange, and peer-to-peer payments. Revolut’s products leverage APIs built by other banking and financial services companies. As a fintech aggregator, Revolut focuses on creating a smooth, user-friendly experience through its applications. Revolut centers its technology on improving personalization and providing financial management tools that aren’t offered by other fintech companies in a single application. In other words, Revolut focuses more on integration than innovation, with its primary value proposition being the user experience.
Stay tuned for more blog posts in this series on AI in fintech and regtech. In Part 3, I’ll provide some practical advice on how your firm can start an AI project. And in Part 4, I’ll wrap up the series by discussing ethical implications of AI development and implementation.
Originally published on joot.io.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.