Artificial intelligence (AI) and machine learning (ML) are playing increasingly important roles in the financial services industry. Their promise of strengthening an operational infrastructure that helps firms to be more agile, innovative, and scalable is driving greater usage. These technologies are already becoming pervasive and getting embedded into daily tasks, such that we may not even know when we are using them. But there still are challenges and opportunities to be met in their growing development and deployment.
To take a deep dive to learn more, we reached out to Institute member Bo Howell, Founder and CEO of Joot - a FinTech company that provides web-based compliance technology and services to registered investment advisers, broker-dealers, and funds. Fresh from finishing a detailed 4-part series of blog posts entitled “Will AI Revolutionize RegTech?” and speaking as a panelist at the recent NSCP National Conference on the topic, Bo offers his practical perspective and thoughts on where we are and where we are going with these new technologies.
Hortz: From your perspective as a FinTech CEO, what do you see as the current state of AI deployment in the industry?
Bo Howell: Many financial services firms still use manual tools like spreadsheets and human-driven analytics to manage their processes, such as internal financial planning and analysis, trend analysis, error monitoring, and more. Manual processes take a lot of time and effort, and most employees find them boring and tedious, making them more error prone. Additionally, many business owners understand that manual processes are inefficient and unproductive, but they don’t know how to leverage machine learning to augment the work of their employees. In fact, many small and mid-sized financial services firms are not aware of the important information that’s available from their client and operational data. AI-driven RegTech tools can automate enough of the data management and trend analysis to allow employees to complete the more complex aspects of their job efficiently and effectively.
Hortz: What do you see that is holding up a greater usage of AI and ML?
Bo Howell: Two major reasons for the slow spread of AI and ML in the financial services industry are a lack of data and the high cost associated with these emerging technologies. Building AI/ML tools requires large data sets to train and refine the underlying machine models. This takes substantial amounts of time, money, and resources that are often in short supply in the market as a whole and, therefore, expensive for SMBs.
Hortz: Are there any new trends starting to change this slow progression to start driving greater usage?
Bo Howell: Three major trends are now changing the game. First, digital adoption has accelerated. Consumers initially hesitant to shop or bank online have rapidly shifted to digital interactions. This acceleration, in turn, changed human behavior and disrupted many preexisting predictive models, leading to a second trend: many companies are rushing to adopt more intelligent predictive models. Third, AI building blocks have become increasingly accessible. These building blocks include pretrained models, curated data sets, and tool sets that make working with data sources like text, documents, images, audio, and video more accessible than ever.
Hortz: How are financial services firms currently leveraging AI in their daily operations and strategic goals?
Bo Howell: Some firms are using AI and ML to develop specific solutions for daily problems while others are taking an aggregator approach. For example, Implementation Management Professionals (IMP) developed its CLEAR Compliance system using a type of ML commonly referred to as natural language processing, or NLP. Their 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.
On the other hand, digital banking unicorn Revolut offers products that leverage APIs built by other banking and financial services companies. As a fintech aggregator, they focus on creating a smooth, user-friendly experience through its applications. They center their 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.
Hortz: How can a financial services firm build a business case to implement AI into their operations? What practical steps can a firm take to start an AI project?
Bo Howell: The first step is developing AI literacy. Before a firm can determine whether it makes sense to start an AI project, it’s helpful to understand where AI can add value and then evaluate the capabilities of the many preexisting AI-powered software platforms. The fundamental question to ask is, “What business process do I need to create or improve?” If a solution doesn’t already exist and it’s related to a core competency of the business, it may make sense to design a new solution.
A few questions are critical to consider at this stage. For example, what questions, behavior, or problems are you addressing? Who will be the product users? Who are the other product stakeholders? What data do you have to train the AI model? What types of algorithms should you use in your data? What metrics do you hope to achieve with your product? All these elements go into a business case, which is needed to convince management, investors, employees, and others to buy into an AI project.
Hortz: What are some practical applications you see for AI specifically in addressing compliance and portfolio management issues?
Bo Howell: One of the ways we have integrated AI into our compliance technology at Joot is as a tool that uses emotional AI and powerful analytics to monitor employee trading. With our Personal Trade Monitor tool, chief compliance officers (CCOs) can visually track employee and company trades on a timeline that marks restricted periods as well as significant news events categorized by sentiment. The tool can sift through thousands of data points and highlight specific areas that need the CCO’s attention.
Another tool called Portfolio Monitor uses natural language processing (NLP) algorithms to analyze thousands of social media posts per second to deliver clear data visualizations that reveal emerging trends in stocks and cryptocurrencies. This tool can be used by portfolio managers or investment advisers to conduct sentiment analysis on issues or trends.
Hortz: Where do you see the development of AI and ML will take financial services firms? How can AI and ML revolutionize the industry?
Bo Howell: AI-driven RegTech tools can help firms spend less time on compliance and generate better, more comprehensive results. To be clear, less time doesn’t mean firms are not meeting their regulatory requirements. Rather, AI/ML can make a compliance program both more efficient and more effective. This now allows firms more time to focus on client engagement, business innovation, and growth. Also, tools like ours that are geared toward small to mid-sized firms make these revolutionary technologies more accessible and affordable to more registrants. And these tools can scale with the firm’s growth plans.
Hortz: Tell us about your assembling an advisory board of tech-savvy investment advisers interested in co-developing AI-driven RegTech tools. How are you structuring this working relationship with advisors and what are your goals and the outcomes you see coming from this initiative?
Bo Howell: At Joot, we deeply value our relationships with our customers and users of our technology. We want the tech tools we develop to work for people, not the other way around. User feedback factors heavily into our product design process. We observe how people use our technology and adapt our products and services to meet their needs and preferences, which evolve over time as they face new regulatory requirements and the tech landscape changes. We see our customers and co-developers as partners in our product development story.
Hortz: Any other advice or suggestions you can share with advisors on how to take advantage of these technologies and apply them to critical functions and risks in their business?
Bo Howell: Many times, firms pursue “AI for the sake of AI.” It’s important to think about AI in terms of human enablement, not process automation. This starts with understanding the task that firms intend to support and determining where precisely AI can add value to their products and services. The best uses of AI/ML are those that free up employees to perform tasks better suited to their talents and interests. Firms will then be well on their way toward implementing effective—and even revolutionary—AI-powered solutions.