Financial Management System for SMEs: Real-World Deployment of Accounts Receivable and Cash Flow Prediction

Abstract:Small and Medium Enterprises (SMEs), particularly freelancers and early-stage businesses, face unique financial management challenges due to limited resources, small customer bases, and constrained data availability. This paper presents the development and deployment of an integrated financial prediction system that combines accounts receivable prediction and cash flow forecasting specifically designed for SME operational constraints. Our system addresses the gap between enterprise-focused financial tools and the practical needs of freelancers and small businesses. The solution integrates two key components: a binary classification model for predicting invoice payment delays, and a multi-module cash flow forecasting model that handles incomplete and limited historical data. A prototype system has been implemented and deployed as a web application with integration into Cluee's platform, a startup providing financial management tools for freelancers, demonstrating practical feasibility for real-world SME financial management.

Machine Learning (cs.LG)

arXiv:2511.03631 [cs.LG]

(or arXiv:2511.03631v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2511.03631 Focus to learn more arXiv-issued DOI via DataCite

Computational Science - ICCS 2026, Lecture Notes in Computer Science, vol. 16784, pp. 351-358, Springer, 2026

https://doi.org/10.1007/978-3-032-29924-6_28 Focus to learn more DOI(s) linking to related resources

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