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

Bartłomiej Małkus, Szymon Bobek, Grzegorz J. Nalepa

TL;DR

The paper tackles SME financial management under data scarcity by integrating accounts receivable payment-delay prediction with cash flow forecasting. It introduces a modular, transparent architecture consisting of AR and four-submodule CF components, deployed as a REST API and integrated with Cluee on Google App Engine. Key contributions include data-efficient feature engineering with moving-average trends, per-customer SVM models, synthetic data generation for realistic evaluation, and a real-world deployment that demonstrates actionable liquidity insights for SMEs. The work shows that an integrated AR+CF approach can provide robust, interpretable forecasts and practical guidance for proactive financial management in resource-constrained small businesses.

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.

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

TL;DR

The paper tackles SME financial management under data scarcity by integrating accounts receivable payment-delay prediction with cash flow forecasting. It introduces a modular, transparent architecture consisting of AR and four-submodule CF components, deployed as a REST API and integrated with Cluee on Google App Engine. Key contributions include data-efficient feature engineering with moving-average trends, per-customer SVM models, synthetic data generation for realistic evaluation, and a real-world deployment that demonstrates actionable liquidity insights for SMEs. The work shows that an integrated AR+CF approach can provide robust, interpretable forecasts and practical guidance for proactive financial management in resource-constrained small businesses.

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.

Paper Structure

This paper contains 18 sections, 4 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Definition and Requirements
  4. SME Constraints
  5. Workflow Needs
  6. System Requirements
  7. Deployed Solution
  8. System Architecture
  9. System Architecture
  10. Accounts Receivable Prediction Module
  11. Cash Flow Forecasting Module
  12. Integration and Deployment
  13. Real-World Performance and Impact
  14. Evaluation Setup and Datasets
  15. Accounts Receivable Prediction Performance
  16. ...and 3 more sections

Figures (1)

  • Figure 1: System architecture showing two prediction workflows: accounts receivable prediction (blue) and integrated cash flow forecasting (green). The blue workflow processes historical invoice data to predict payment delays for individual invoices. The green workflow integrates accounts receivable predictions into cash flow forecasting, where the system predicts future invoices, estimates their payment delays, and incorporates these delays into comprehensive cash flow predictions with explanatory insights. The cash flow module consists of four sub-modules handling different income and expense categories.