Bad Financial Data Is AI’s Biggest Liability

§ 01 Executive Snapshot

• What: AI's effectiveness in finance is hindered by poor data quality and legacy systems.
• Who: Ben Catterall, global head of solutions engineering at Fynapse, and multinational payments clients.
• Why it matters: Understanding and utilizing financial data accurately is crucial for operational efficiency and leveraging AI in finance.

§ 02 Key Developments

• 46% of AI proofs of concept fail to reach production due to poor data quality, limiting effectiveness.
• A multinational payments client processed transactions across nearly 18 countries, uncovering foreign exchange spreads averaging about 2%.
• The loss on $100 million of cross-border payments could be $2 million a year due to these spreads.

§ 03 Strategic Context

• Legacy finance systems struggle to keep pace with the complexity of modern payment types and transaction volumes, leading to operational inefficiencies.
• Financial institutions need to treat data as core infrastructure and not just a byproduct of payment processes to support growth and maintain financial control.

§ 04 Strategic Implications

• Immediate consequences include the need for finance teams to gain transaction-level visibility to avoid revenue losses and improve operational performance.
• Long-term implications suggest that firms modernizing their finance data architecture will be better positioned for future operational demands and AI integration.

§ 05 Risks & Constraints

• Potential risks include reliance on outdated legacy systems that impede timely financial reporting and data accuracy.
• Competition may arise from firms that can adapt more quickly to modern finance data requirements and AI integration, outpacing those with poor data practices.

§ 06 Watchlist / Forward Signals

• Future developments will signal the success of data modernization efforts, particularly in how firms handle transaction-level visibility and AI deployment.
• Rollout timelines for new AI initiatives and data architecture improvements will be critical to monitor for their impact on financial performance and operational capabilities.