AArtificial intelligence (AI) and machine learning (ML) are playing an increasingly important role in the financial services industry. Their promise to build an operational infrastructure that helps businesses be more agile, innovative, and scalable is driving increased usage. These technologies are already becoming ubiquitous and integrating into everyday tasks, so we may not even know when we are using them. But there are still challenges and opportunities to be met in their increasing development and deployment.

To deepen our knowledge, we contacted a member of the Institute Bo Howell, Founder and CEO of Joot – a FinTech company that provides web-based technology and compliance services to registered investment advisers, broker-dealers and funds. Fresh off the end of a detailed 4-part blog post series titled “Will AI revolutionize RegTech? » and speaking as a panelist at the recent NSCP National Conference on the subject, 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 companies still use manual tools such as spreadsheets and human analytics to manage their processes, such as internal financial planning and analysis, trend analysis, error monitoring, etc. 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 leaders understand that manual processes are inefficient and unproductive, but they don’t know how to leverage machine learning to increase the work of their employees. In fact, many small and medium-sized financial services companies are unaware of the important insights available from their customer and operational data. AI-powered RegTech tools can automate data management and trend analysis enough to allow employees to complete the most complex aspects of their jobs effectively and efficiently.

Horz: What do you think is holding back greater use of AI and ML?

Bo Howell: Two main reasons for the slow spread of AI and ML in the financial services industry are the lack of data and the high cost associated with these emerging technologies. Creating AI/ML tools requires large datasets to train and refine underlying machine models. It takes a lot of time, money and resources that are often scarce in the market as a whole and therefore costly for SMEs.

Horz: Are there any new trends that are starting to change this slow progression to start increasing 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 quickly turned to digital interactions. This acceleration has, in turn, changed human behavior and disrupted many pre-existing predictive models, leading to a second trend: many companies are rushing to adopt smarter predictive models. Third, the building blocks of AI have become increasingly accessible. These building blocks include pre-trained models, curated datasets, and toolsets that make working with data sources like text, documents, images, audio, and video more accessible than never.

Horz: How are financial services companies currently leveraging AI in their day-to-day operations and strategic goals?

Bo Howell: Some companies use AI and ML to develop specific solutions to everyday problems while others take an aggregation approach. For example, Implementation Management Professionals (IMP) developed their CLEAR compliance system using a type of ML commonly known as natural language processing, or NLP. Their CLEAR compliance system helps asset managers facilitate critical components of their investment transaction 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 created by other banking and financial services companies. As a fintech aggregator, they focus on creating a smooth and user-friendly experience through its apps. They focus their technology on improving personalization and providing financial management tools that are not offered by other fintech companies in a single application. In other words, Revolut focuses more on integration than innovation, with its main value proposition being user experience.

Horz: How can a financial services company make a business case for implementing AI in their operations? What practical steps can a company take to start an AI project?

Bo Howell: The first step is to develop AI literacy. Before a business can determine whether it makes sense to start an AI project, it helps to understand where AI can add value and then assess the capabilities of the many pre-existing software platforms powered by the AI. The fundamental question to ask is: “What business process should I create or improve?” If a solution does not already exist and is tied to a core business competency, it may make sense to design a new solution.

A few questions are essential to consider at this stage. For example, what issues, behaviors or issues are you addressing? Who will be the users of the product? Who are the other product stakeholders? What data do you have to train the AI ​​model? What kinds of algorithms should you use in your data? What measurements do you hope to achieve with your product? All of these go into a business case, which is needed to convince management, investors, employees, and others to buy into an AI project.

Horz: What practical applications do you see for AI specifically to solve 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 transactions. With our Personal Trade Monitor tool, Chief Compliance Officers (CCOs) can visually track employee and company transactions on a timeline that marks restricted time periods as well as important news events ranked by sentiment. The tool can sift through thousands of data points and highlight specific areas that require CCO attention.

Another tool called Portfolio Monitor uses natural language processing (NLP) algorithms to analyze thousands of social media posts per second to provide clear data visualizations that reveal emerging trends in stocks and cryptocurrencies. This tool can be used by portfolio managers or investment advisors to perform sentiment analysis on issues or trends.

Horz: Where do you think the development of AI and ML will take financial services companies? How can AI and ML revolutionize the industry?

Bo Howell: AI-powered RegTech tools can help businesses spend less time on compliance and drive better, more comprehensive results. To be clear, less time does not mean companies are not meeting their regulatory requirements. On the contrary, AI/ML can make a compliance program both more efficient and more effective. This now gives companies more time to focus on customer engagement, business innovation and growth. Plus, tools like ours for small and medium-sized businesses make these game-changing technologies more accessible and affordable to more enrollees. And these tools can scale with the company’s growth plans.

Horz: Tell us about your building an advisory board of tech-savvy investment advisors interested in co-developing AI-powered RegTech tools. How do you structure this working relationship with advisors and what are your goals and the results you see coming from this initiative?

Bo Howell: At Joot, we deeply value our relationships with our customers and the users of our technology. We want the technology tools we develop to work for people, not the other way around. User feedback plays an important role in our product design process. We watch how people use our technology and adapt our products and services to meet their needs and preferences, which change over time as they face new regulatory requirements and the changing technology landscape. We view our customers and co-developers as partners in our product development story.

Horz: Any other tips or suggestions you can share with advisors on how to take advantage of these technologies and apply them to their critical business functions and risks?

Bo Howell: Often, companies pursue “AI for AI’s sake”. It’s important to think of AI in terms of human empowerment, not process automation. It starts with understanding the task that companies intend to take on 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. Companies will then be well on their way to implementing effective, even revolutionary, AI-powered solutions.

Institute for Innovation Development is an educational and business development catalyst for growth-oriented financial advisors and financial services firms committed to leading their businesses in an operating environment characterized by accelerating business and cultural change. We position our members with the necessary continuous innovation resources and best practices to drive and facilitate their growth, differentiation, and unique next-generation customer/community engagement strategies. The institute was launched with the support and foresight of our founding sponsors – Ultimus Fund Solutions, NASDAQ, Pershing, Fidelity, Voya Financial and Charter Financial Publishing (publisher of Financial Advisor and Private Wealth magazines).

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.