Developing an Intelligent Framework for Corporate Cash Flow Management Using Predictive Analytics and Optimization Algorithms
Keywords:
Cash flow management, financial intelligence, predictive analytics, optimization algorithms, corporate treasury, data governance, working capitalAbstract
This study aimed to develop a qualitative framework for intelligent corporate cash flow management by integrating predictive analytics, optimization algorithms, data governance, and evidence-based financial decision-making. This qualitative exploratory study was conducted among financial managers, treasury managers, accounting managers, financial data analysts, and corporate finance consultants working in Tehran-based companies. Participants were selected through purposive and snowball sampling. Data were collected through 22 semi-structured interviews, after which theoretical saturation was achieved. Each interview lasted approximately 45 to 75 minutes and focused on cash flow management challenges, financial data capabilities, predictive modeling, optimization-based decision-making, and implementation requirements for intelligent cash management systems. The interviews were transcribed verbatim and analyzed using thematic analysis with the support of NVivo software. To enhance trustworthiness, member checking, peer review, audit trail documentation, and constant comparison of codes were employed. The analysis resulted in five main categories: integration and quality of cash flow data, predictive analytics and liquidity scenario planning, optimization of treasury and working capital decisions, algorithmic governance and model controllability, and organizational readiness for adopting intelligent systems. Participants emphasized that intelligent cash flow management is not limited to software deployment or dashboard visualization; rather, it requires coherent data architecture, integration of operational and financial data, decision-rule design, explainable models, and active involvement of financial managers in validating algorithmic outputs. The findings indicate that an intelligent cash flow management framework becomes effective when forecasting, optimization, and data governance are designed as an integrated system. The proposed framework can help companies identify liquidity shortages earlier, optimize payment and collection decisions, determine optimal cash balance levels, and enhance financial resilience under uncertainty through data-driven decision-making.
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