Dinuwan Fernando

Dinuwan Fernando

With a background in software engineering, Dinuwan Fernando specializes in leveraging machine learning for finance. His final year project at the University of Plymouth, titled "Machine Learning Approach for Corporate and Retail Lending," focused on creating an automated, unbiased, and efficient system for loan approval and credit scoring. His model, based on the XGBoost algorithm, was designed to address the inefficiencies and biases in traditional lending practices in Sri Lanka. This innovative solution featured a GUI allowing users to input key variables for immediate loan approval predictions, significantly reducing time and increasing transparency and fairness in credit evaluations.

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