A major bank wants to better predict the likelihood of default for its customers, as well as identify the key drivers that determine this likelihood. They hope that this would inform the bank’s decisions on who to give a credit to and what credit limit to provide, as well as also help the bank have a better understanding of their current and potential customers, which would inform their future strategy, including their planning of offering targeted credit products to their customers.
The bank collected data on 25 000 of their existing clients. Of those, 1,000 were randomly selected to participate in a pilot described below. Data about the remaining 24,000 is in the file “Credit_data.csv”. The dataset contains various information, including demographic factors, credit data, history of payment, and bill statements from April to September, as well as information on the outcome: did the customer default or not in October.