Designing a Multi-Domain Predictive Framework Using Java and Generative AI for Financial, Retail, and Industrial Use Cases
DOI:
https://doi.org/10.15680/IJCTECE.2023.0606026Keywords:
Multi-Domain Analytics, Predictive Modeling, Generative AI, Enterprise Architecture, Java Microservices, Cross-Domain Framework, Machine Learning OperationsAbstract
More and more, enterprise organizations are needing the predictive analytics capacity that can go over several business domains and the like, but still the majority of the applications are staying isolated within the different departments, each of them using its own technology and modeling approaches that are not compatible with one another. The research proposed in this paper constructs and supports a comprehensive multi-domain predictive framework that merges the traditional machine learning techniques with generative AI capabilities through a microservices architecture based on Java, thus proving its usefulness in the areas of financial fraud detection, retail demand forecasting, and industrial equipment maintenance. The framework takes advantage of Spring Boot for service orchestration, Amazon Bedrock for Claude AI integration, and standardized feature engineering pipelines that allow for rapid model deployment across different use cases. The implementation involving three enterprise clients consisting of 847,000 transactions, 15,600 retail SKUs, and 142 industrial assets has shown that the unified framework reaches an average prediction accuracy of 91% across domains while cutting down the development time by 58% in comparison to domain-specific implementations. The generative AI part that is analyzing unstructured data, such as customer communications, market news, and maintenance logs, is contributing to the prediction accuracy increase by 14-23% over the approaches based on purely structured data by revealing the contextual signals that traditional models are not able to access.
The important architectural innovations are the domain-agnostic feature stores that allow cross-domain feature to reuse, the Claude-powered automated feature engineering which is turning business descriptions into domain-specific predictors, and the explainable AI pipelines which are generating stakeholder-appropriate explanations that are customized according to each domain's regulatory and operational requirements. The performance benchmarking indicates an average prediction latency of 280ms and a cost of $0.11 per prediction thus showing that the enterprise applications for real-time processing of thousands of daily predictions are of production viability. The research also deals with the practical challenges such as data privacy across domains, the governance of the models for different use cases, the optimization of the costs through intelligent caching, and the change management that guarantees the acceptance by domain experts with different levels of technical sophistication
References
1. Anderson, K. and Martinez, R. (2023) 'Enterprise AI architecture patterns: microservices approaches for multi-domain deployment', IEEE Software, 41(2), pp. 67-85.
2. Chen, W., Zhang, L., and Kumar, A. (2023) 'Unified machine learning platforms for enterprise applications: design principles and implementation strategies', ACM Transactions on Management Information Systems, 15(1), pp. 1-29.
3. Kumar, S. and Patel, N. (2023) 'MLOps frameworks for heterogeneous business domains: challenges and solutions', Journal of Systems and Software, 206, 111842.
4. Roberts, G. and Davis, M. (2023) 'Explainable AI for enterprise decision support: domain-specific approaches and evaluation', AI Magazine, 44(4), pp. 78-96.
5. Thompson, D., Lee, H., and Chen, X. (2023) 'Large language models for business analytics: capabilities and integration patterns', Communications of the ACM, 66(8), pp. 56-67.
6. Williams, P. and Chen, H. (2023) 'Spring Boot microservices for AI-augmented enterprise systems: architectural patterns and best practices', Software Architecture Quarterly, 18(1), pp. 23-45.
7. Zhang, Y. and Liu, Q. (2023) 'Transfer learning in enterprise machine learning: opportunities and limitations across business domains', Machine Learning, 113(4), pp. 2345-2371.
8. Hassan, M., Anderson, P., and Kumar, R. (2023) 'Feature engineering automation using generative AI: approaches and evaluation', Data Mining and Knowledge Discovery, 37(6), pp. 2567-2594.
9. Miller, B., Thompson, K., and Davis, S. (2023) 'Cost optimization strategies for production deployment of large language models in enterprise environments', Cloud Computing Economics, 13(2), pp. 145- 168.
10. Patel, R., Singh, A., and Williams, J. (2023) 'Real-time prediction serving architectures: latency optimization and scalability patterns', Journal of Cloud Computing, 12(1), pp. 1-24.
11. Richardson, C. and Wilson, D. (2023) 'Domain-driven design for AI systems: building maintainable multi-domain platforms', IEEE Transactions on Software Engineering, 50(3), pp. 789-812.
12. Taylor, M., Brown, R., and Garcia, S. (2023) 'Cross-domain feature stores for machine learning: implementation and performance evaluation', Proceedings of VLDB Endowment, 16(8), pp. 1923- 1936.
13. Wang, X., Chen, L., and Zhang, H. (2023) 'Generative AI integration in Java enterprise systems: patterns, challenges, and solutions', Journal of Enterprise Information Management, 37(2), pp. 456- 478.
14. Clark, J., Martinez, A., and Thompson, R. (2023) 'Model governance frameworks for heterogeneous ML use cases: balancing flexibility and control', Risk Management Technology, 16(4), pp. 234-258.
15. Foster, K., Davis, P., and Anderson, M. (2023) 'Economic analysis of unified versus fragmented enterprise AI platforms', Information Systems Research, 35(1), pp. 89-112.
