AI-Powered Cloud-Native Software Development for Mortgage Loan Transformation: Leveraging Serverless ETL and SAP HANA Analytics

Shravan Uday Chatterjee

doi:10.15662/IJRAI.2023.0606009

Authors

Shravan Uday Chatterjee Department of Computer Engineering, SIT, Pune, India Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606009

Keywords:

AI-Powered Software Development, Cloud-Native Architecture, Mortgage Loan Transformation, Serverless ETL, SAP HANA Analytics, Machine Learning, Deep Reinforcement Learning, Intelligent Automation, Predictive Risk Management, Microservices

Abstract

The modernization of mortgage loan systems requires intelligent, scalable, and data-centric architectures capable of handling complex financial workflows in real time. This paper presents an AI-powered cloud-native software development framework that leverages serverless Extract, Transform, and Load (ETL) processes and SAP HANA analytics to transform mortgage loan operations into adaptive, data-driven ecosystems. The proposed model integrates machine learning (ML) and deep reinforcement learning (DRL) within a microservices-based cloud architecture, enabling predictive credit risk assessment, intelligent loan approval automation, and dynamic interest rate optimization. By utilizing serverless computing, the framework minimizes infrastructure overhead while maximizing flexibility and fault tolerance across hybrid and multi-cloud environments. SAP HANA serves as the in-memory analytical engine, supporting real-time financial insights, compliance validation, and automated decision intelligence. Experimental evaluations demonstrate enhanced processing efficiency, reduced latency, and improved accuracy in mortgage portfolio management. This research establishes a foundation for next-generation digital mortgage ecosystems that combine AI, cloud-native development, and advanced analytics to achieve operational resilience, cost-effectiveness, and intelligent automation in the financial services sector.

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