Transforming finance with AI
The discussion began with Jared Brown emphasizing the critical role of AI in financial services and the importance of identifying precise use cases. “No one goes looking for a solution looking for a problem,” said Brown, emphasizing the need for financial institutions to carefully select AI applications that address real needs and ensure AI contributes to meaningful transformation rather than becoming an over-hyped buzzword. Brown's insights set the tone for a session aimed at demystifying AI and exploring its practical applications.
Brown also detailed the evolution of the finance function within the organization. “The finance function has changed, but the organization doesn't see it that way,” Brown said, noting that there is a time lag between the adoption of the new role and the recognition of that role by other departments. This discrepancy highlights the need for effective communication and visibility regarding the finance team's strategic contributions.
Anna Gozdalik-Coakley shared her expertise on the ethical impact and regulatory challenges posed by AI. “AI can accelerate and delegate repetitive tasks,” she said, highlighting the benefits of AI in compliance and internal controls. Gozdalik-Coakley described a use case where AI significantly reduced the legal team's manual workload by 70%, freeing up resources for more strategic tasks. This example shows how AI can increase efficiency while maintaining ethical standards.
Gozdaric-Coakley also emphasized the importance of transparency and human oversight in AI operations. “Without effective communication, finance teams are perceived as gatekeepers of data, rather than a resource for strategic business insights,” she explained. She advocated for regular cross-department briefings and the use of visual tools, such as dashboards, to make financial data accessible and understandable to all departments.
AI in the Cryptocurrency Industry
Cormac Dinan and AI Blockchain Regarding technology, he said, “AI is being applied both internally and externally in the cryptocurrency space.” Dinan explained how AI can enhance transaction monitoring of blockchain transactions and ensure data integrity. By leveraging the blockchain's structured data, AI can provide robust security and efficiency, highlighting the complementary nature of these technologies.
Dinan also emphasized the potential for AI to transform the cryptocurrency industry. “AI is not just about customer service chatbots. It’s about managing large data sets and improving operational efficiency,” he said. He emphasized the importance of combining AI with blockchain to create a more secure and reliable financial system.
Transforming Retail Finance
Colin Creagh of Klarna explained how AI is transforming retail finance by improving customer experience. “The buzzword is removing friction from interactions,” Creagh explained. Klarna uses AI to personalize shopping experiences and streamline customer service. The integration of AI has enabled Klarna to handle more than two-thirds of customer service queries with chatbots, significantly improving response times and customer satisfaction.
Krieg gave several examples of how AI is being used within Klarna. “Our in-house AI, Kiki, processes approximately 2,000 questions every day, streamlining our internal processes and improving customer interactions,” he said. He also mentioned how AI is helping generate marketing content, reducing the time needed to create a campaign from six weeks to just seven days. This level of efficiency demonstrates the transformative potential of AI in retail finance.
Compliance and Regulation
Gozdaric Coakley spoke about the compliance challenges facing financial institutions in the age of AI. “AI can help with both external and internal compliance,” he said, noting that AI can manage knowledge, monitor regulatory changes, and validate internal frameworks. He also emphasized that AI can act as an accelerator for compliance functions, processing vast amounts of data efficiently and accurately.
Gozdaric Coakley cited a successful example of 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 investigative workload by 70% and significantly increased productivity,” she explained.
The panelists also discussed the ethical challenges of deploying AI in finance. Jared Brown pointed out the importance of data quality and fairness in AI operations. “Accurate data is essential for fairness in AI operations,” Brown said. The panelists agreed that transparency, human oversight, and robust data governance are essential to address these ethical concerns.
The need for continuous monitoring and quality assurance is crucial: “AI in financial services is high-risk and requires continuous monitoring to ensure compliance and mitigate risk,” noted one of the panelists. They advocated for the development of scenario-based models that show the financial impact of different departmental decisions, helping to bridge the gap between finance and other departments.
AI in Finance: A Look to the Future
AI has great potential to transform finance. But realizing this potential requires careful consideration of ethical, regulatory and operational challenges. Financial institutions need to adopt a balanced approach that harnesses the power of AI while ensuring transparency and fairness.
Discussions at the Dublin Tech Summit provided valuable insights into the future of AI in finance. Panelists focused on real-world applications, ethical considerations and regulatory compliance, highlighting the way forward to embrace innovation while adhering to core principles.
For finance professionals, the message was clear: AI is not just an efficiency tool, it is a strategic asset that can drive significant advances in the industry – but its success depends on responsible implementation and a commitment to ethical practices.
