Why Non-Deterministic AI Cannot Fix Legacy Data Fragmentation in Transaction Networks

Relying on advanced generative intelligence to bypass legacy technical debt is a high-risk strategy for modern financial institutions. In this segment from the FF News Virtual Arena, host Ian Horne sits down with data and product leaders from Raiffeisen Bank International, PAYABL, and Torus to discuss why structural data integrity is the defining factor in transaction profitability.

The panellists examine the operational costs of maintaining unstructured data collections, explaining how un-utilized data directly damages a bank's competitiveness in merchant pricing markets. They address the technical realities of Large Language Models, detailing why non-deterministic algorithms are prone to hallucinations that can cause significant financial errors if deployed without strict boundaries. Discover how forward-thinking institutions are building hybrid data frameworks—combining highly accurate, deterministic core reconciliation engines with conversational AI interpretation layers—to safely optimize conversion rates, refine pricing models, and drive sustainable corporate growth.

Read the full article here: https://ffnews.com/fintech-tv/overcoming-data-fragmentation-and-the-limits-of-ai-in-transaction-profitability/

Видео Why Non-Deterministic AI Cannot Fix Legacy Data Fragmentation in Transaction Networks канала FF News | Fintech Finance