Dynamic Liquidity Routing in SAP In-House Banking Using AI-Driven Cash Flow Intelligence

This article presents an implementation of an AI-driven framework for automating liquidity routing within the context of SAP In-House Banking (IHB) systems. Treasurers within multinational enterprises are increasingly challenged by managing fragmented liquidity across global subsidiaries, which increases the need for intelligent, real-time orchestration of treasury functions. This study presents an architecture that integrates AI cash flow forecasting models with SAP's cash concentration and netting processes, enabling proactive decision-making for intra-group liquidity allocation and routing. The system trains forecasting models such as ARIMA, XGBoost, and LSTM on structured features like bank statements, invoice flows, and treasury triggers, achieving notable results in transaction timing forecasting and routing efficiency. Simulating over 100,000 liquidity events tested the framework's scalability and yielded results demonstrating a 48% reduction in average time-to-route, a 37% improvement in liquidity utilization rates, and a 41% reduction in netting inefficiency compared to benchmarked arbitrary rule-based flowing accounting systems. The system also demonstrated warm load resilience and out-of-the-box bottleneck exception recovery, which outperformed the monotonic method's cross-entity bottleneck exception recovery. These findings highlight the opportunity and potential impact of integrating AI-driven deep liquidity intelligence into SAP IHB systems, laying the groundwork for the later integration of digital wallets, CBDCs, and cross-border AI treasury agents into real-time finance systems.