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

2025-11-05

AI Summary

Small and medium-sized enterprises (SMEs) and freelancers face significant challenges in cash flow management due to data scarcity and limited computational resources, rendering mainstream enterprise-grade financial tools impractical. To address this, we propose a lightweight, two-module forecasting architecture tailored for few-shot learning scenarios: a front-end machine learning model performs binary classification of accounts receivable delay risk, while a back-end modular time-series model—designed to handle incomplete historical records—enables fine-grained cash flow forecasting. The system is deployed as a web application with deep integration into the Cluee financial platform. Evaluated in real-world operational settings, the prototype demonstrates substantial improvements in prediction accuracy and decision responsiveness under sparse-data conditions. This work bridges a critical gap in intelligent financial control for micro- and small-scale entities, both technically and practically.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• Predicts invoice payment delays for SMEs
• Forecasts cash flow with limited historical data
• Bridges enterprise tools and small business needs

Innovation

• Binary classification model predicts invoice payment delays
• Multi-module cash flow forecasting handles limited historical data
• Web application integrated into financial management platform