Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders. Homelify strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Homelify makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.
This is a standard supervised classification task: Supervised: The labels are included in the training data and the goal is to train a model to learn to predict the labels from the features Classification: The label is a binary variable, 0 (will repay loan on time), 1 (will have difficulty repaying loan).
The data is provided by Home Credit, a service dedicated to provided lines of credit (loans) to the unbanked population.