By: Ancora

While generative AI and Large Language Models (LLMs) make for impressive product demos, applying them to Accounts Payable automation introduces distinct trade-offs. This position paper from ancora Software examines why general-purpose, probabilistic LLMs — which estimate likely answers rather than guaranteeing exact figures — are fundamentally misaligned with high-stakes financial processing where totals, banking details, and line items require absolute accuracy.

Beyond data privacy, security, and added API costs, routing confidential financial data through third-party LLMs often introduces unnecessary operational complexity. The paper makes the case for a layered, purpose-built AI architecture that pairs targeted machine learning with Small Language Models (SLMs). By capturing customer- and vendor-specific corrections directly, this hybrid approach allows systems to learn continuously from routine user feedback without relying on third-party generative models.  

Read the position paper here:
Use of AI Techniques in Intelligent Document Processing