A Conversation with MUFG Pension and Market Services: Managing Reconciliations and Treasury Risk at Scale
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We caught up with Ian Taylor, Treasury Reconciliation Manager at MUFG Pension and Market Services, to learn more about his experience with managing hundreds of thousands of transactions every month at scale and the future of AI in finance.
Operating at Extreme Scale: Treasury Reconciliation in a Global Bank
Q: Ian, could you introduce yourself and describe your role at MUFG?
A: My name is Ian Taylor, and I work in the Treasury reconciliation space at MUFG Pension and Market Services. Prior to last year, the business operated as Link Group / Link Asset Services, but we are now part of MUFG. We’ve been using Trintech since 2011, so it’s been a long partnership. I’ve personally been with the organization for more than five years, which has given me a strong perspective on how the solution has evolved and how we use it today.
The Challenge: Reconciling Thousands of Bank Accounts across Platforms
Q: What makes reconciliation particularly challenging in your environment?
A: Scale and complexity. We manage approximately 5,000 bank accounts, working across multiple external banking partners and internal platforms. That level of complexity requires a solution that’s both flexible and robust—especially when it comes to creating matching rules that can reconcile as much activity as possible while reducing operational risk.
Why Trintech: Flexibility, Matching Rules, and Risk Reduction
Q: How does Trintech support those needs?
A: The flexibility of the matching rule engine is critical for us. Being able to configure and refine rules allows us to reconcile large volumes efficiently and reduce risk across treasury operations. In environments like ours—where some organizations are processing hundreds of thousands of transactions per month—any improvement in matching accuracy or automation delivers immediate value.
Looking Ahead: AI, Controls, and Governance
Q: How do you see AI influencing reconciliation in the future?
A: AI is a really interesting area for us, but the conversation always comes back to controls and governance.
From a rule-matching perspective, AI presents opportunities to become more dynamic—analyzing data patterns and improving auto-matching rates over time. That kind of analysis can help teams continuously refine rules and reduce risk. At the same time, in a regulated banking environment, it’s essential that AI-driven decisions are well-governed, auditable, and controlled. When implemented correctly, AI can play a key role in delivering safer, more efficient reconciliation processes.
