Intelligent System and Framework for Integrating Machine Learning with Software Development for Predictive Banking Logistics

Efficient management of financial resources is vital for the sustainable operation of banks, particularly in optimizing policy acquisition to ensure liquidity and enhance financial planning. This paper introduces an intelligent decision support system utilizing machine learning (ML) to predict policy acceptance, aiming to reduce acquisition costs, improve customer retention, and support small and medium-sized enterprises (SMEs) with more favorable loan terms. Our approach integrates the CRISP-DM methodology with Continuous Integration and Continuous Deployment (CI/CD) processes, leveraging agile Scrum practices to ensure iterative development and rapid deployment. Ensemble learning techniques (combining Neural Networks, Random Forest, and Support Vector Machines) are employed to achieve high predictive accuracy. The system's effectiveness is demonstrated through experiments using the Bank Marketing Dataset, with results validated by standard quality metrics. Additionally, a user-friendly dashboard and REST API have been developed to facilitate efficient client identification and model deployment. The V-model is applied to ensure rigorous testing and validation throughout the project lifecycle. This comprehensive approach enhances internal logistics, optimizes resource management, and supports SME growth, thus fostering economic development and job creation. Our work provides a robust framework for integrating ML solutions and agile methodologies in banking and similar sectors.