Transforming finance with AI
The discussion kicked off with Jared Browne emphasising the critical role of AI in financial services and the importance of identifying precise use cases. “No one wants a solution in search of a problem,” said Browne, highlighting the necessity for financial institutions to carefully select AI applications that address real needs, ensuring that AI contributes to meaningful transformation rather than becoming an overhyped buzzword. Browne’s insights set the tone for a session that aimed to demystify AI and explore its practical applications.
Browne also elaborated on the evolution of the finance function within organisations. “The finance function has moved, but organizations do not view them as such,” he noted, pointing out the lag between the adoption of new roles and the recognition of these roles by other departments. This discrepancy underscores the need for effective communication and visibility of the finance team’s strategic contributions.
Anna Gozdalik-Coakley shared her expertise on the ethical implications and regulatory challenges posed by AI. “AI can accelerate and delegate repeatable tasks,” she noted, highlighting the benefits of AI in compliance and internal governance. Gozdalik-Coakley described a use case where AI significantly reduced the manual workload of legal teams by 70%, freeing up resources for more strategic tasks. This example illustrated how AI could enhance efficiency while maintaining ethical standards.
Gozdalik-Coakley also stressed the importance of transparency and human oversight in AI operations. “Without effective communication, finance teams are perceived as gatekeepers of data rather than as resources for strategic business insight,” she explained. She advocated for regular inter-departmental briefings and the use of visual tools like dashboards to make financial data accessible and understandable to all departments.
AI in the crypto space
Cormac Dinan brought the conversation to the intersection of AI and blockchain technology. “We’re seeing AI applied both internally and externally in crypto,” he stated. Dinan discussed how AI enhances transaction monitoring and ensures data integrity in blockchain transactions. By leveraging structured data from blockchain, AI can provide robust security and efficiency, underscoring the complementary nature of these technologies.
Dinan also highlighted the potential of AI to transform the crypto sector. “AI is not just about customer service chatbots; it’s about managing large datasets and improving operational efficiencies,” he said. He emphasised the importance of combining AI with blockchain to create more secure and reliable financial systems.
Transforming retail finance
Colin Creagh from Klarna discussed how AI is transforming retail finance by enhancing customer experiences. “The buzzword is about taking friction out of the interaction,” Creagh explained. Klarna uses AI to personalise shopping experiences and streamline customer service. The integration of AI has allowed Klarna to handle over two-thirds of customer service queries through chatbots, significantly improving response times and customer satisfaction.
Creagh provided several practical examples of how AI is being used within Klarna. “Our in-house AI, Kiki, handles nearly 2,000 questions daily, streamlining internal processes and improving customer interaction,” he said. He also mentioned how AI helps in generating marketing content, reducing the time required to create campaigns from six weeks to just seven days. This level of efficiency demonstrates AI’s transformative potential in retail finance.
Compliance and regulation
Gozdalik-Coakley addressed the compliance challenges faced by financial institutions in the AI era. “AI can help with both external and internal compliance,” she said, pointing out that AI can manage knowledge, monitor regulatory changes, and verify internal frameworks. She also stressed that AI could act as an accelerator in compliance functions, handling vast amounts of data efficiently and accurately.
Gozdalik-Coakley provided an example of a successful AI implementation in a financial institution. “We recently helped a client with a group of 90 lawyers managing 200,000 legal documents. AI reduced their research workload by 70%, significantly enhancing productivity,” she explained.
The panellists also discussed the ethical challenges of deploying AI in finance. Jared Browne pointed out the importance of data quality and fairness in AI operations. “Accurate data is essential for fair AI operations,” he said. The panellists agreed that transparency, human oversight, and robust data governance are crucial to addressing these ethical concerns.
The need for ongoing monitoring and quality assurance is crucial. “AI in financial services is high-risk and requires continuous oversight to ensure compliance and mitigate risks,” noted one of the panellists. They advocated for the development of scenario-based models to show the financial impact of various departmental decisions, helping to bridge the gap between finance and other departments.
AI in finance: Looking forward
AI holds immense potential for transforming finance. However, realising this potential requires careful consideration of ethical, regulatory, and operational challenges. Financial institutions must adopt a balanced approach, leveraging AI’s capabilities while ensuring transparency and fairness.
The discussion at the Dublin Tech Summit provided valuable insights into the future of AI in finance. By focusing on real-world applications, ethical considerations, and regulatory compliance, the panelists highlighted a path forward that embraces innovation while safeguarding fundamental principles.
For financial professionals, the message was clear: AI is not just a tool for efficiency but a strategic asset that can drive significant advancements in the industry. However, its success depends on responsible implementation and a commitment to ethical practices.
