MANUFACTURING MANAGEMENT SYSTEM: FINANCIAL MANAGEMENT SYSTEM (FINANCIAL SECURITY, ADAPTIVE OPTIMIZATION AND ANOMALY DETECTION USING MACHINE LEARNING AI)

Abstract

JJM Soap and Detergents Manufacturing faced persistent challenges in manual financial practices, including inefficiencies, data inconsistencies, and lack of real-time visibility. To address these concerns, this study developed a cloud-based Financial Management System (FMS) tailored to JJM’s operational needs. The system emphasizes three core pillars: financial security, adaptive financial optimization, and anomaly detection using machine learning. Features such as multi-factor authentication, encryption, and role-based access control ensure secure transactions. Meanwhile, AI-driven models optimize capital management and detect fraudulent activities, integrating financial intelligence into broader departmental operations like HR and logistics for enhanced transparency and strategic decision-making. The FMS was developed using the Agile Scrum framework and microservices architecture. Key modules including Cash Management, Accounts, Ledger, and Admin Control were built using the MERN stack (MongoDB, Express.js, React.js, Node.js). Continuous integration and deployment were implemented using GitHub and Render. Security features included HTTPS, multi-factor authentication (MFA), role-based access control (RBAC), and audit logs. Machine learning algorithms supported real-time fraud detection and budgeting recommendations. RESTful APIs ensured seamless integration across departments. User acceptance testing (UAT) was conducted to validate system performance and security compliance. The implementation of the FMS successfully resolved major financial management issues at JJM. All modules operated in real time and maintained a 99.5% system uptime. Security mechanisms effectively protected sensitive financial data, while AI-driven tools improved decision-making and reduced errors. The anomaly detection module achieved 93% accuracy in identifying potential fraud. Users reported improved transparency, more rational financial management, and enhanced reporting accuracy. UAT confirmed the system’s functionality and compliance with security protocols. The FMS demonstrated the effectiveness of digital transformation in modernizing financial processes within manufacturing. Automation reduced delays and errors, while AI technologies facilitated intelligent budgeting, anomaly detection, and financial forecasting. Although initial challenges included user adaptation and data migration, the Agile approach ensured system success. The scalable and cloud-ready architecture not only addressed JJM’s current financial needs but also positioned the company for future advancements such as predictive analytics. This study highlights the value of integrating AI-powered financial systems into manufacturing management to drive efficiency, security, and data-driven decisions.