Is AI in Finance Hype or a Game-Changer?
Recorded live at the TradingTech Summit in Canary Wharf, this episode of FinTech Focus TV features an insightful conversation between host Toby Babb and Monika Fernando, Head of Global FI Client Data Analytics & Head of FI eTrading Strategy EAP at TD Securities. As AI continues to shape the financial sector, this discussion explores whether AI is merely a trend or a transformative force in financial markets. The conversation delves into AI adoption, workplace transformation, risk management, and the future of virtual assistants within trading technology.
AI Adoption in Financial Institutions: Hype vs Reality
One of the most compelling discussions at the TradingTech Summit was the state of AI adoption in financial institutions. Monika Fernando began by discussing the results of a live audience poll conducted during her panel. The poll revealed a split in perception: one-third of attendees reported that their firms were successfully leveraging AI to drive value, while another third admitted they were struggling to achieve tangible benefits. This disparity highlights the different stages of AI maturity within the financial sector. Some institutions have fully embraced AI to automate operations and enhance efficiency, while others are still working through the challenges of adoption.
For FinTech firms and financial institutions alike, AI is currently being applied in three main areas: legal and regulatory compliance, automation of documentation and reporting, and productivity enhancement. AI is particularly effective in streamlining regulatory reporting and document summarisation, allowing firms to meet compliance requirements more efficiently. However, despite these advantages, the adoption journey remains fraught with hurdles, particularly in relation to regulatory concerns and risk management.
The Role of AI in Risk and Compliance
The financial industry operates in an environment of strict regulatory oversight, making risk control a top priority for institutions adopting AI. Monika highlighted that while AI offers significant efficiency gains, many firms are cautious about fully integrating AI-driven decision-making into their operations. One of the major roadblocks is the need for explainability—regulators require institutions to justify AI-generated decisions. If a regulator questions a trade decision made by an AI model, financial firms must be able to provide a clear and precise explanation.
This challenge has led many institutions to approach AI with caution. Rather than deploying AI in high-risk areas such as trading decisions, firms are primarily using AI for automation and augmentation of workflows. AI can assist in generating reports, analysing market trends, and streamlining internal processes, but financial institutions remain hesitant to entrust AI with critical decision-making responsibilities.
Augmentation, Not Replacement: AI’s Role in Financial Workflows
A common concern surrounding AI is the fear of job displacement. However, Toby and Monika explored how AI is currently being used to enhance rather than replace human roles. AI is automating repetitive tasks such as data processing and regulatory reporting, but it is not eliminating the need for human oversight. Instead, it is allowing financial professionals to focus on higher-value tasks that require strategic thinking and expertise.
This transformation in workflows presents a significant opportunity for workplace evolution. Monika emphasised that effective communication from leadership is crucial in ensuring that employees understand AI’s role as a supportive tool rather than a replacement. Firms that successfully integrate AI do so by fostering a culture of transparency and education, ensuring that employees see AI as a means to enhance their efficiency rather than a threat to job security.
Early Adopters: Identifying AI Champions in Financial Institutions
A key factor in AI adoption is the identification of early adopters within an organisation. These individuals do not necessarily come from technical backgrounds but demonstrate an interest in experimenting with AI tools. Monika shared an anecdote from the panel discussion about an HR professional with a Shakespearean acting background who became an AI advocate within his company. Despite lacking a technical background, he successfully developed an AI-powered chatbot to handle HR queries, proving that AI tools are now accessible to non-technical users.
This example highlights the importance of empowering employees to experiment with AI solutions. Financial firms looking to integrate AI effectively should focus on training employees, providing them with opportunities to engage with AI tools, and encouraging innovation. By allowing employees to experiment and iterate on small-scale AI projects, organisations can drive broader adoption and unlock AI’s full potential.
Data Lakes, Cloud Solutions, and AI Models: The Foundations of AI in Finance
For financial institutions to successfully leverage AI, they need to establish a solid technological foundation. According to Monika, three critical areas should be prioritised:
- Data Lakes: Centralising structured and unstructured data in a secure, well-organised repository is essential for AI to deliver accurate insights. A clean, structured data environment enhances AI’s ability to generate meaningful analysis and decision-making support.
- Cloud Partnerships: Collaborating with cloud providers such as AWS, Google Cloud, and Microsoft Azure allows financial institutions to scale AI operations effectively while reducing infrastructure costs. Cloud solutions enable faster deployment and improved accessibility for AI applications.
- Pre-Built AI Models: Rather than developing AI models from scratch, firms can leverage pre-trained models such as ChatGPT or Google’s Gemini AI. By training these models on proprietary financial data, firms can deploy AI solutions more efficiently and cost-effectively.
AI and Strategic Partnerships: The Future of Financial Technology
As AI adoption continues to accelerate, Monika predicts that 2025 will be marked by a rise in strategic partnerships between financial institutions and technology providers. Firms that embrace AI-driven innovation early will gain a competitive edge, positioning themselves ahead of those that hesitate. These partnerships will enable organisations to scale AI solutions faster, leverage cutting-edge technology, and drive operational efficiencies.
Toby and Monika also discussed the evolution of AI-powered virtual assistants and chatbots. While many current chatbot solutions remain frustrating for users, AI is rapidly advancing in this space. By 2025, chatbots and virtual agents are expected to become significantly more sophisticated, intuitive, and effective, providing enhanced customer service and improving business efficiency.
AI’s Acceleration in 2025: What’s Next for Financial Institutions?
Looking ahead, the financial sector is expected to experience even faster AI adoption. Monika emphasised that today’s large language models (LLMs) are the worst they will ever be—meaning that AI capabilities will only improve. As AI technology advances, financial firms will need to make strategic decisions on how best to integrate AI while maintaining regulatory compliance and risk management.
The discussion also touched on broader economic and political factors influencing AI’s rapid acceleration. Increased investment in AI, combined with technological breakthroughs, will create an environment where firms that adopt AI early will have a distinct advantage over competitors. Those that lag behind risk falling out of step with the industry’s rapid evolution.
This episode of FinTech Focus TV provides a comprehensive exploration of AI’s role in financial institutions, covering risk control, compliance, workplace transformation, and strategic AI partnerships. Monika Fernando’s insights underscore the importance of early adoption, regulatory alignment, and a strong technological foundation.
For financial firms, the message is clear: AI is not just a trend—it is a transformative force. Organisations that embrace AI strategically, invest in employee training, and establish the right partnerships will be at the forefront of financial technology innovation.
One of the most striking takeaways from this episode is the varying levels of AI adoption in finance. Monika shared insights from a live poll taken during her panel discussion, revealing that while some firms are making significant strides with AI, others struggle to generate measurable value. This disparity highlights the challenges of integrating AI within regulated industries like banking and trading.
AI is already being used in regulatory compliance, automation, and productivity enhancement, but its full-scale adoption remains a work in progress. Monika explains that risk control and regulatory concerns are the primary obstacles to AI’s wider implementation, as financial firms must ensure transparency and explainability in AI-driven decisions.
A major focus of the discussion was the role of AI in augmenting financial workflows rather than replacing jobs. AI is enabling professionals to work more efficiently by automating repetitive tasks, allowing employees to focus on high-value, strategic initiatives. However, successful adoption depends on clear communication from leadership, ensuring employees view AI as a tool for enhancement rather than a threat to job security.
Monika also emphasised the importance of early adopters within organisations—those willing to experiment with AI tools. She shared an example of a non-technical HR employee who successfully implemented an AI chatbot, demonstrating that AI solutions are becoming increasingly accessible.
For Harrington Starr, a leading FinTech recruitment company, AI’s evolution is directly influencing hiring trends across Cloud Engineering and DevOps, Cyber Security and IT risk, Data, Infrastructure support, Network Engineering, Product management, Quantitative Finance, Sales and Marketing, and Software engineering. As AI reshapes financial workflows, demand is rising for professionals who can navigate the challenges of AI implementation, data analytics, and regulatory compliance.
Financial institutions and FinTech firms are seeking experts who can develop AI-driven strategies, manage IT risk, and enhance infrastructure support to drive digital transformation. Businesses need talent skilled in Cloud Engineering and DevOps to optimise AI models, Cyber Security professionals to ensure compliance, and Software Engineers to build AI-powered solutions. Similarly, Quantitative Finance and Data specialists are crucial for leveraging AI in trading and risk management.
As AI adoption accelerates, companies require professionals who can implement AI solutions, drive product management strategies, and enhance sales and marketing efforts to remain competitive. Harrington Starr is at the forefront of this transformation, connecting businesses with the talent they need to embrace AI, improve efficiency, and lead in the evolving financial landscape.
