Probability Graphical Model for Predicting Probability of Default For Mortgage Loans

Abstract

Assessment of Default risk of borrowers is important for lending institutions as it directly affects profits and losses of the firm and guides in compensating the risk by taking appropriate majors for loans having higher probability of default. Predicting probability of default using statistical and machine learning models has been a popular research topic in data science community. While different types of classification models have been proposed historically, there is scope to apply probabilistic inference to the mortgage default analysis to support decision making. Probabilistic Graphical Model (PGM) are a powerful framework for compactly encoding probability distributions over complex multivariate domains using graphical representations. Due to the high interpretability and inherent support for probabilistic inference, the PGM models have widely been used under various domains such as medical diagnosis, text, audio, video processing.