Prediction of Accounts Receivables for Customers Using Enhanced Machine Learning Models

For any business to operate in today’s competitive business environment efficient credit risk management is imperative to prevent financial instability and loss of revenue because of late payments. This paper describes an end-to-end intelligent system for customer risk profiling and classification on accounts receivable data through machine learning techniques. I will first start by preprocessing raw invoice data for extraction of key customer behavioral metrics, that is, number of invoices, days late, dispute counts, etc., as well as average invoice value. The customers payment patterns are used to group customers into Low, Medium and High-risk levels using K-Means clustering algorithm. Then under these risk levels, we use them as labels to train classification models such as Random Forest, Support Vector Machine (SVM), and Decision Tree classifiers. An interactive Flask based web application with SQLite integration, featuring user authentication, risk dashboards, as well as customer specific predictions, is also a part of the system. By simply inputting a customer ID, users gain the ability to understand payment trends and receive insights about the risk for any given customer. matplotlib and seaborn are used for data visualization and joblib is used to save trained models and deploy it to use in generating predictions. Businesses can gain a powerful proactive tool for credit risk assessment and make better decisions and have better control over their financials with this integrated framework.