Artificial Intelligence-Driven Optimization of Accounts Receivable Management in Supply Chain Finance: An Empirical Study Based on Cash Flow Prediction and Risk Assessment

Liya Ge

Journal of Sustainability, Policy, and Practice, 2025, vol. 1, issue 2, 110-120

Abstract: The integration of artificial intelligence technologies in supply chain finance has emerged as a critical factor for enhancing operational efficiency and financial performance. This study presents a comprehensive framework for optimizing accounts receivable management through AI-driven methodologies, focusing on cash flow prediction and risk assessment capabilities. Traditional accounts receivable management systems face significant challenges in processing large volumes of financial data and accurately predicting customer payment behaviors in dynamic market conditions. Our research develops a multi-dimensional approach combining machine learning algorithms, feature engineering techniques, and risk assessment frameworks to address these limitations. The proposed methodology integrates various data sources, including historical transaction records, customer behavior patterns, and external market indicators, to create robust predictive models. Experimental results demonstrate significant improvements in cash flow prediction ac-curacy, with the implementation achieving a 23.7% reduction in bad debt provisions and an 18.2% improvement in collection efficiency compared to traditional methods. The empirical analysis validates the effectiveness of the proposed AI-driven approach across different industry sectors and company sizes. This research contributes to the advancement of supply chain finance optimization by providing practical solutions for real-world implementation and demonstrating measurable financial benefits for organizations adopting AI-enhanced accounts receivable management systems.

Keywords: artificial intelligence; accounts receivable management; supply chain finance; cash flow prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:1:y:2025:i:2:p:110-120

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