Predictive Analytics for Working Capital Management: Machine Learning Applications in Cash Flow and Liquidity Forecasting

Abstract

This study addresses the persistent problem that finance teams in cloud-enabled and ERP-integrated enterprises often lack decision-grade visibility into short-horizon cash flow and liquidity, because working-capital timing is driven by heterogeneous receivables and payables behaviors that are poorly captured by simple rules or baseline statistical forecasts. The purpose was to quantify, using a quantitative cross-sectional, case-based evidence map, how predictive analytics and machine learning improve cash flow and liquidity forecasting for working capital management across enterprise cases documented in the literature. The study design treated each documented application instance as a “case” and synthesized N = 35 cases spanning cloud and enterprise data ecosystems (ERP, treasury workflows, and banking-adjacent settings), with sector-identifiable enterprise contexts reported in 18 of 35 cases (51.4%). Key variables included forecasting target (AR cash-in, cash position, AP cash-out, liquidity shortfall), model family (boosting/ensembles, regression, SVM, deep sequence, hybrid), feature strategy (aging, calendar, behavioral history, bank-reconciliation signals), and implementation conditions (governance, explainability, workflow integration, monitoring). The analysis plan used frequency distributions, cross-tabulation-style comparisons, and hypothesis-aligned evidence-strength scoring, plus improvement-rate coding versus baselines. Headline findings show ML outperformed statistical or rule baselines in 24/35 studies (68.6%) with strongest support for H1 (4.2/5), while tree-based ensembles were most prevalent (57.1%) and had the highest within-family improvement rate (75.0%). Feature enrichment mattered: among cases using aging structures, 82.6% reported better performance when behavioral or process-state features were added (evidence strength 4.0/5). For liquidity stress handling, hybrids were tested in 40.0% of cases and outperformed single models under volatility proxies in 71.4% of that subset (evidence strength 3.6/5). Practical implications are that enterprises gain the most when ML forecasting is embedded into governance and workflows: governance detail appeared in 42.9% of cases with 80.0% uptake, and workflow integration appeared in 40.0% with 78.6% uptake, indicating that trust and integration convert accuracy gains into operational impact.