Optimizing Invoice Distribution Through Machine Learning and Natural Language Processing Techniques

This paper introduces DebtSight, a cutting-edge web application created to completely transform how companies anticipate and handle debt collection cases resulting from invoice distributions. By examining invoice distribution data and projecting payment patterns, the program offers predictive insights into debt collection scenarios. This paper discusses the design, implementation, and evaluation of DebtSight, a predictive analytics and natural language interaction tool that uses OpenAI’s GPT-3.5-turbo-0125 and advanced machine learning models, respectively. DebtSight addresses the increasing difficulty that companies encounter in handling overdue invoices, which frequently lead to reduced cash flow and strained relationships with customers. The system offers early alerts of possible debt collection situations by using predictive modelling to invoice distributions, enabling companies to take preventative action. The application’s frontend is created with Angular, and its backend is designed in Python and uses SQL databases for data administration. DebtSight also incorporates LangChain for seamless data retrieval and NLP-enabled queries. DebtSight’s capacity to converse with users in natural language is one of its primary features. This allows users to ask queries regarding the status of invoices and receive conversational responses in return. This improves the usability of complex data by increasing its accessibility and usefulness. Additionally, the technology takes ethical factors into account, such GDPR compliance, guaranteeing that client data is handled appropriately. A dataset with 100,000 entries representing different invoice distributions was used for the project. Date column cleaning, feature selection, and non-numerical variable encoding were all part of the data preprocessing process. To predict payment outcomes, machine learning models like Gradient Boost (GB), Logistic Regression (LR), and Random Forest Classifier (RFC) were trained on the data. With an accuracy of 82.22%, the Gradient Boost model outperformed the other models in terms of trial outcomes, especially when the invoice amount was divided into bins. Gradient Boost is a useful technique for prioritizing debt collection operations because precision and recall measures demonstrated that it efficiently detected real positives with few false positives. GB consistently performed better than RFC and LR, particularly when managing non-linear relationships within the data. DebtSight offers tremendous possibilities for wider commercial application by successfully enhancing the efficacy and efficiency of debt collection procedures while upholding ethical standards.