Dataset aimed to improve in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal is to build model that borrowers can use to help make the best financial decisions.
• 150,000 borrowers
Dataset structure:
ID: ID of borrower
Default: Person experienced 90 days past due delinquency or worse (Type: Y/N).
Balance on credit cards: Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits (Type: percentage)
Age: Age of borrower in years (Type: integer)
Number of times due in 30-59 Days. Number of times borrower has been 30-59 days past due but no worse in the last 2 years. (Type: integer).
DebtRatio: Monthly debt payments, alimony, living costs divided by monthly gross income (Type: integer)
MonthlyIncome: Monthly income (Type: real)
Number of loans: Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards) (Type: integer)
NumberOfTimes90DaysLate: Number of times borrower has been 90 days or more past due. (Type: integer)
NumberRealEstateLoansOrLines: Number of mortgage and real estate loans including home equity lines of credit (Type: integer)
NumberOfTime60-89DaysPastDueNotWorse: Number of times borrower has been 60-89 days past due but no worse in the last 2 years. (Type: integer)
NumberOfDependents: Number of dependents in family excluding themselves (spouse, children etc.). (Type: integer)
