Predictive Analytics of Accounts Receivable to Strengthen Cash Flow Forecasting via Credit Risk Evaluation
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P121Keywords:
Account Receivable, Credit Risk Assessment, Cash Flow Forecasting, Machine Learning, GRU, Financial Risk Management, Feature Engineering, Deep LearningAbstract
Enhancement in the businesses that employ accounts receivable for liquidity management, credit risk assessment and cash flow forecasting has been a major financial priority. The paper develops a predictive framework that utilizes a credit risk dataset generated from the historical accounts receivable records, which includes payment timelines, ageing schedules, and financial behavioral variables. The data is prepared by removing duplicates, handling missing values, label encoding, feature engineering and normalization to guarantee that the data is reliable and appropriate for training the model a proposed model GRU-based deep learning model which makes a strong impression by achieving 98.67% accuracy, 97.69% precision, 96.95% recall, and an F1-score of 97.67%, the model was compared with Neural Networks (87.2%), Decision Tree (89.8%), LGBM (96.8%), and Naïve Bayes (90.2%) the GRU model exhibits superior prediction ability since it can successfully capture a temporal dependency in accounts receivable data deep learning methods together with financial instruments significantly facilitates credit risk assessment and cash flow forecasting. The proposed approach is highly beneficial for the use of predictive analytics in accounts receivable management and data-driven financial decision-making.
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