Our Solution
A popular short term lending portal empowers mortgage brokers to compete with banks and large retail lenders, by providing predictive technology and tools. Its CoreLogic model predicts pre-payments and loan defaults. We did the segmentation to increase the accuracy to predict delinquency of a loan using credit bureau data, customer transaction data, and customer behavior data. A GBM model was built to predict the front-end debt-to-income ratio (FEDT), which is key for predictive delinquency. Further, we experimented with 160 variations of GBM models with Machine Learning techniques using the Caret package. Various visualizations were also done to compare the actual data set and the predicted data set.