Predictive Credit Default Risk Clustering Model

Develop a sophisticated SQL-based machine learning clustering algorithm that predicts potential credit defaults by analyzing complex financial profiles. Implement k-means clustering using window functions to segment loan applicants into risk categories, incorporating variables like debt-to-income ratio, credit history, employment stability, and macroeconomic indicators. The solution must handle non-linear relationships and provide a probabilistic risk scoring mechanism with at least 85% accuracy.