Predictive Cash Flow Management in Oracle ERP Cloud Using Machine Learning
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I3P106Keywords:
Oracle ERP Cloud, Machine Learning, Predictive Analytics, Cash Flow Management, Accounts Receivable, Accounts Payable, LSTM, Gradient Boosting, Financial Forecasting.Abstract
In contemporary business, cash flow is important in ensuring the health and viability of the business. Manual inputs and rigid forecasting models, which characterize the traditional cash flow forecasting methods, have limited the use of traditional forecasting methods, as most of them are inaccurate and thus cause inaccuracies, which are reflected in the operations of the business. In this paper, a machine learning-redesigned, predictive model is proposed to automate cash flow with automation at a higher degree in the module Accounts Receivable (AR) and the Accounts Payable (AP) in Oracle ERP Cloud. The model uses an analysis of the past trends of AR/AP along with payment behavior of the customers and payment terms, to predict the future flows in and out more accurately and with less manual input. Our approach is to employ supervised learning models, specifically Gradient Boosting Machines (GBM), the Long Short-Term Memory (LSTM) network and ensemble techniques, which are trained on ERP transactional data. The proposed system deploys on Oracle ERP Cloud APIs, making it easier to consume and update data in the model at run-time. Our experimental results demonstrate that our predictive model is able to increase accuracy of cash flow forecasting significantly over that achieved using a rule-based approach. The system offers dashboard visualization to the finance team, such as scenario analysis and risk scores. The proposed research also brings much value to the existing literature because it shows that ML can be used to enhance ERP systems and provide timely financial forecasts to open up the possibilities of intelligent enterprise resource planning and financial automation. The feasibility and effectiveness of the proposed approach are tested with the help of a prototype implementation that is carried out using Python, Oracle Cloud Infrastructure, and Oracle Autonomous Database
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