AI-Driven Financial Forecasting Using SAP ERP in Large Enterprises

Authors

Ravi Kumar Perumallapalli

Abstract

The protection of large-scale networks faces challenges due to sophisticated cyber attacks. Traditional cybersecurity solutions often fall short in addressing the complexity of contemporary threats. This study presents an AI-enhanced cybersecurity architecture that incorporates advanced machine learning techniques to improve existing security protocols. The framework aims to enhance threat detection, response, and prevention capabilities. It includes essential elements such as data collection, preprocessing, feature engineering, model training, real-time deployment, and a feedback loop. Network traffic data is collected and cleaned in the preprocessing stage, while critical attributes indicating possible dangers are extracted for advanced machine learning models. These models monitor network traffic in real-time to identify irregularities, and detected threats improve the model's performance through feedback. Tests show significant improvements in detection accuracy (98%) and response times (under two seconds), with scalable performance across various operational contexts. Future enhancements may include advanced algorithms and the integration of blockchain technology and quantum-resistant algorithms for secure data sharing. Collaborating with industry stakeholders will be key to customizing the framework for real-world applications. This AI-enhanced approach represents a notable advancement in cybersecurity for protecting vital infrastructure in a digital age.

Keywords

AI-Driven Financial Forecasting, SAP ERP Integration, Large Enterprise Financial Management, Predictive Analytics in Finance, Machine Learning for Financial Forecasting

AI-Driven Financial Forecasting Using SAP ERP in Large Enterprises. Ravi Kumar Perumallapalli. 2018. IJIRCT, Volume 4, Issue 1. Pages 1-8. https://www.ijirct.org/viewPaper.php?paperId=2411014