Motivated by the increasing volatility in the Canadian housing market, rising interest rates, and the tightening regulatory landscape, this study explores the application and comparative analysis of artificial intelligence (AI) models for assessing mortgage risk, in particular, default risk prediction. Traditional techniques, such as credit scoring and financial ratios, often fail to capture the intricate, non-linear relationships and shifting borrower behaviors characteristic of modern mortgage portfolios. AI-driven models, on the other hand, excel at processing complex datasets and uncovering hidden patterns. This research evaluates multiple AI approaches to assess their predictive accuracy, adaptability, and interpretability, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Random Forests (RF), and XGBoost. Results demonstrate that ensemble models, particularly the class-weighted XGBoost model, deliver superior adaptability to volatility ( i.e ., volatile housing market), while neural networks show potential when applied to rich datasets but demand significant computational resources. By improving borrower risk prediction and enabling proactive adjustments to loan terms, AI models help financial institutions reduce default rates, achieve regulatory compliance, and optimize operational costs. These models enhance the financial sector’s resilience to market volatility while paving a way towards more sustainable lending practices. This study highlights the need to balance predictive performance, interpretability, and adaptability, offering insights for leveraging AI in effective, data-driven mortgage risk management.
Sayad et al. (Thu,) studied this question.