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Next-Gen Finance: The Evolution of Fintech | 2026 Cornerstone Conference
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Financial services professionals and tech strategists exploring AI-driven transformation.
TL;DR
Fintech has evolved from a niche category to the core of financial services, with AI driving real-time, personalized systems. Panelists discuss challenges like legacy infrastructure, cybersecurity, and the need for continuous innovation. Insights from global leaders highlight the importance of trust and regulatory guardrails.
Key Takeaways
- Fintech is no longer a separate category; technology is now embedded in every financial transaction, from payments to insurance claims, making it synonymous with financial services.
- Legacy systems built for batch processing and scheduled maintenance struggle to support 24/7 real-time operations, requiring a fundamental rearchitecture for resilience and continuous uptime.
- The shift from human-led to AI-driven system access via APIs expands the attack surface, demanding new identity and cybersecurity approaches to manage data and operational risks.
- Korea’s success in fintech stems from world-class ICT infrastructure, high smartphone penetration, and integrated ecosystems that enable super apps embedding finance into daily life.
- Alternative credit scoring using non-financial data, like bookstore purchase history, can expand financial inclusion but requires navigating complex regulatory approvals to ensure fairness.
- The real driver of fintech innovation is not technology alone but the underlying infrastructure and regulatory environment that enable seamless integration and trust.
- AI and real-time payments are forcing financial institutions to move from quarterly releases to multiple daily updates, transforming software development and operational stability.
- Trust in AI-driven financial ecosystems is tested by immediate global concerns over data bias and cybersecurity, making guardrails essential to prevent moving too fast without safeguards.
In This Video
- 00:00Welcome and Session Introduction
Moderator Margareta McGrath welcomes attendees and sets the stage for fintech discussion.
- 00:42Fintech Becomes Financial Services
Technology is now embedded in every transaction, with AI fundamentally transforming finance.
- 01:45Introducing the Panelists
Four leaders from cybersecurity, operations, policy, and AI transformation join the panel.
- 05:13Legacy Systems and AI Gaps
Jess Calvin discusses challenges of layering modern tech on century-old infrastructure.
- 08:22Korea's Fintech Innovation
Kungju Hu explains Korea's super app ecosystem and alternative credit scoring.
Questions & Answers
What are the biggest gaps between legacy systems and new fintech?
Legacy systems use batch processing and quarterly releases, but fintech needs real-time 24/7 access, continuous updates, and identity management for AI agents, creating operational, stability, and data risks.
How is fintech evolving differently in Korea?
Korea built super apps integrating taxi, gifts, and finance, driven by fast ICT, 95% smartphone penetration, and deregulation. An alternative credit scoring model used bookstore data to offer lower loan rates.
What is the role of AI in financial services today?
AI is transforming finance from digitalization to real-time, personalized ecosystems. It's layered onto century-old systems, requiring rearchitecture for resilience, identity, and security.
How do you build trust in AI-driven financial ecosystems?
Trust is tested by data bias and cybersecurity issues. These are no longer theoretical but immediate global challenges that need guardrails as we move fast.
What are the risks of moving too fast in fintech?
Risks include data, stability, and operational risks from layering 21st-century tech on 20th-century infrastructure, especially with 24/7 uptime and agentic AI.
Key Terms
- Agentic AI — AI systems that act autonomously, accessing systems via APIs instead of human-led identity.
- Super app — An integrated platform combining multiple services like taxi, gifts, and finance in one app.
- Alternative credit scoring — Using non-traditional data, like purchase history, to assess creditworthiness and offer lower rates.