Sustainable Mortgage Loan Automation: Explainable AI, Risk Analytics, and Scalable Software Development for Inclusive Banking
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Abstract
The integration of Explainable Artificial Intelligence (XAI) into mortgage loan automation has revolutionized risk assessment and decision-making processes in the banking sector. This paper presents a sustainable, scalable, and transparent AI-driven software development framework for inclusive mortgage loan management. The proposed model leverages machine learning, natural language processing, and rule-based reasoning to enhance interpretability, improve risk prediction accuracy, and promote trust among stakeholders. Furthermore, the study emphasizes sustainable IT operations that reduce computational energy footprints while ensuring data privacy, compliance, and accessibility. Through a modular cloud-native architecture, the system supports adaptive learning, real-time analytics, and inclusive financial practices, particularly targeting underbanked populations. The results indicate significant improvements in credit scoring transparency, reduced bias in loan approval processes, and enhanced sustainability through optimized resource utilization.
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