LoanLens: A Multimodel AI Architecture for Secure and Transparent Loan Decisioning using Feature Engineering
Keywords:
Explainable AI, Financial technology, Fraud detection, Loan approval prediction, Loan recommendation system, Machine learningAbstract
The growing complexity of modern lending requires intelligent, automated systems to ensure accuracy, efficiency, and security in loan processing. This paper introduces LoanLens, an AI-powered decision support platform that unifies loan recommendation, approval prediction, and fraud detection within a single framework. For approval prediction, LoanLens employs logistic regression, random forest, and XGBoost, while fraud detection is powered by an XGBoost-based anomaly detection model that identifies irregular applications. To provide relevant options, the system dynamically scrapes loan schemes from multiple banking websites and generates real-time, personalized recommendations aligned with applicant profiles. Transparency and trust are reinforced through explainable AI (XAI) techniques, particularly SHAP, which enable stakeholders to interpret model outcomes. Built with Python, FastAPI, and Next.js, the platform features a scalable architecture that ensures smooth integration between backend analytics and frontend interaction. Experimental evaluation shows LoanLens achieves high predictive accuracy, reliably flags fraudulent cases, and delivers effective recommendations, improving customer experience while reducing institutional risk.
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