Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article introduces a conceptual framework for artificial intelligence-driven accounts receivable automation that addresses persistent inefficiencies in B2B payment processes. The three-layer model encompasses intelligent data extraction using computer vision and natural language processing, predictive payment analytics leveraging machine learning and survival analysis, and automated reconciliation with ERP integration. A composite case study demonstrates how organizations implementing this framework achieve substantial improvements in operational efficiency, working capital management, and business relationships. The implementation architecture leverages cloud-based platforms with robust security frameworks while carefully designed workflows ensure seamless user experiences. Beyond direct operational benefits, the article identifies strategic advantages through network effects that enhance customer and supplier relationships, creating ecosystem-wide improvements in B2B commerce.