About this episode

The episode discusses transforming fragmented payment data into actionable treasury intelligence through AI and advanced analytics.

In this episode, Craig Jeffery speaks with Nathan Dixon of Deluxe about how treasury and accounts receivable teams can transform fragmented payment data into actionable intelligence. They contrast traditional process-focused approaches with data-centric approaches, exploring how AI and advanced analytics can improve cash application, reduce exceptions, identify payer risk, accelerate forecasting, and uncover trends that would otherwise remain hidden. The discussion examines the role of APIs, AI agents, data normalization, natural language interfaces, and machine learning in modern receivables operations. Nathan explains how organizations can move from reactive exception management to proactive decision-making by leveraging clean, centralized data and AI-driven insights. They also discuss data security, model governance, synthetic data, and practical ways treasury and finance professionals can begin experimenting with AI tools today. Company Websites : Strategic Treasurer: https://strategictreasurer.com Deluxe: https://www.deluxe.com

People in this episode

Host: Craig Jeffery

Guest: Nathan Dixon

Topics covered

• payment data
• treasury intelligence
• AI in finance
• data analytics
• cash application
• data security

Keywords

• payment data
• treasury
• AI
• analytics
• cash application
• data normalization
• machine learning
• forecasting
• payer risk

Mentioned in this episode

Organizations: Deluxe, Strategic Treasurer

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