Research Article
Bio-Inspired Optimized Prediction of Invoice Payment Delays in MSMEs
@INPROCEEDINGS{10.4108/eai.19-12-2025.2362547, author={Akash Raghuvanshi and Harsh Upadhyay and Arpit Arpit}, title={Bio-Inspired Optimized Prediction of Invoice Payment Delays in MSMEs}, proceedings={Proceedings of the 1st International Conference on Computational Techniques in Data Science, IICTDS 2025, 19-20 December 2025, Chandigarh, India}, publisher={EAI}, proceedings_a={IICTDS}, year={2026}, month={11}, keywords={Invoice Payment Delay Artificial Intelligence CatBoost Accuracy Grasshopper Optimization Algorithm (GOA) Corrugated Box Manufacturing SME Finance}, doi={10.4108/eai.19-12-2025.2362547} }- Akash Raghuvanshi
Harsh Upadhyay
Arpit Arpit
Year: 2026
Bio-Inspired Optimized Prediction of Invoice Payment Delays in MSMEs
IICTDS
EAI
DOI: 10.4108/eai.19-12-2025.2362547
Abstract
Invoice payment delays are a major issue to deal cash flow challenge in small and medium-sized enterprises (SMEs). In this paper a novel intelligent and optimized predictive framework is proposed to composed CatBoost Based GOA as optimization algorithm. The solu tion offers real time guidance by considering historical invoice data, client likelihood to pay and includes custom-engineered features: payment consistency, macroeconomic trends, and season ality. Experimental results on actual SME invoice datasets show that the metaheuristic-optimized ensemble models can get 93% AUC and outperform other models. The proposed model can effectively enhance convergence rate and accuracy.
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