In the ever-evolving landscape of financial crime, AI and data analytics are starting to become crucial tools for businesses. In addition to assisting organisations with better understanding their customers, it can also help to prevent threat actors from accessing sensitive information, as well as better protect digital systems.
With this in mind, AI Magazine spoke with Roger Burkhardt, Chief Technology Officer (CTO) of Capital Markets and AI at Broadridge. Burkhardt oversees Broadridge’s “learning by doing” mantra, which sees the company provide access to powerful generative AI tools via a single platform.
As a result, the company has been able to rapidly develop award-winning products such as its own BondGPT for corporate bond traders, as well as many internal productivity use cases.
Tell us about your career background and your current role within Broadridge
“I joined Broadridge in 2021 as CTO. In my role I am not only responsible for AI across the firm including our new Generative AI platform – BondGPT, but also for all technologies in Capital Markets from trading through clearing.
“I have over 20 years of experience leading technology transformations and innovating with AI, and have held roles as CTO, CDAO, and CEO. Prior to joining Broadridge I spent four years as the co-lead of McKinsey’s AI practice for financial services driving large scale programs within the top four financial institutions in the US and Europe.
“Before McKinsey I was the CEO of Ingres Corporation, an AI FinTech specialising in insurance, helping build the most accurate models in the P&C industry. I have also worked as CTO at the NYSE where I led the historic digital transformation from floor-based to electronic trading.”
Please explain the significance of artificial intelligence (AI) for the financial services industry
“While AI has long been embedded in the core processes of the financial services industry, from lending decisions to fraud prevention and highly personalised client services for years, the current democratisation of Generative AI has sparked tremendous interest from all stakeholders in the industry.
“Now, with the emergence of ChatGPT, generative AI has massively accelerated and democratised access to powerful foundation models, reducing the cost to build bespoke solutions and opening up a new range of individual productivity applications.
“There is a lot of interest and willingness to adopt AI tools in the sector, but many financial service firms are still grappling with how to capture value while still ensuring security, accuracy, and compliance in a very regulated industry. It is a difficult balance for financial service organisations to tackle – as they still need to adapt to innovations impacting AI early to remain competitive but cannot rush the process and introduce risk.”
What practical steps can financial institutions take to implement successful generative AI?
“When it comes to practical steps that financial institutions can take to implement successful generative AI, IT leaders need to keep people in mind. It’s important for IT leaders to invest in their staff by providing access to GenAI tools, appropriate training, and guidance through the process so that they can rapidly learn by doing.
“Additionally, in order to implement generative AI successfully, financial institutions need to understand the full picture of where AI has the potential to be helpful within their organisation. I recommend that leaders develop a tiered investment process that covers the dynamic range from significant investments required for major efficiency plays or product investments, all the way down to small functional work group initiatives.
“By understanding exactly how AI will fit internally in an organisation, the implementation of AI tools can be more targeted and better suited to increase efficiency and drive revenue.”
How can businesses address the risks of generative AI? What is needed?
“With all technology implementation there is risk. One of the best ways to deal with risk is to approach it proactively. For example, businesses should adopt and publish principles for responsible use of AI with an “evergreen” process to evolve to match the pace of industry innovation and consumer and legal expectations internally.
“It is also important to tier risk management controls based on the potential harm. For example, high stakes decisions such as loan approvals need a higher level of control than low stakes productivity use cases, such as creating personalised communications or systems documentation – as if you get the former wrong, you run the risk of losing customers.
“Given that, it is critically important to address risk by its potential to harm the overall business strategy in order to properly understand which areas you have room to play around with, and which are mission-critical functions with no room for error.”
Moving forward, how do you see the financial services industry using AI?
“As it continues to grow in popularity, AI technology holds the potential to reshape the industry by automating tasks and enhancing decision making, as both stakeholders and consumers push for its implementation.
“More specifically, I believe leaders that can adapt AI within their organisation successfully will drive revenue growth through hyper-personalised client engagement. This in turn will drive down customer servicing costs and improve customer satisfaction with self-service bot-based investments.
“Of course, there is also a lot of potential for AI to help financial service companies provide internal platforms that drive broad based efficiency gains including software development and content generation.”
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