Research Article

Research on the Method of Predicting Consumer Financial Loan Default Based on the Big Data Model

Table 1

Selection and construction of control variables.

Indicator nameActual value of indexQuantitative value of index

GenderMale and femaleMale—0, female—`1
AgeActual value25, 35, 45, 50, 60, 70
OccupationCivil servants, employees of public institutions; other industry staff, soldiers, workers, farmers, civil01, 02, 03, 04, 05, 06, 07, 08, 09, 10
Education levelUnknown, doctor and above, master postgraduate, undergraduate, technical secondary school, high school, junior high school01, 02, 03, 04, 05, 06
Marital statusUnknown, unmarried, marriedUnknown—1
Unmarried—2
Married—3
Annual incomeActual value1, 2, 3, 4, 5, 6, 7, 8, 9
Loan amountActual value1, 2, 3, 4, 5, 6, 7, 8, 9
Loan termActual valueActual value
Lending rateActual valueActual value
Ratio monthly repayment to incomeActual valueSet to 1 below 20% and add 1 for each subsequent 10% increase
Average monthly payrollActual valueDivided by reference to annual income
Number of loan performanceActual valueActual value
Monthly loan frequencyActual valueActual value