Credit risk refers to the potential financial loss incurred when a borrower fails to meet their contractual repayment obligations. Effectively managing credit risk is a fundamental component of risk management for financial institutions. In recent decades, credit risk assessment, particularly in consumer lending, has become increasingly data- and model-driven. Financial institutions now rely heavily on credit scoring models to predict the likelihood of delinquency or default across a range of consumer credit products, including mortgages, auto loans, credit cards, and personal loans. In this study, we present a comprehensive literature review of existing research methods and machine learning (ML) techniques applied specifically to mortgage credit scoring models, with a focus on how Explainable Artificial Intelligence (XAI) can enhance the robustness and transparency of predictive modeling. We find that only a limited number of studies have incorporated XAI methods into mortgage credit scoring, highlighting a significant gap in the current literature. Moreover, although many individual studies include macroeconomic indicators, such as interest rates, unemployment rates, and inflation, as well as climate-related variables, there is a notable absence of a comprehensive framework that organizes and classifies these variables. To address this shortcoming, we propose a structured categorization intended to serve as a practical reference for researchers and practitioners seeking to incorporate these factors more systematically into future credit risk models.