Research Article

Research on Efficiency in Credit Risk Prediction Using Logistic-SBM Model

Table 3

Index correlation.

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1DEA_score1.000.030.010.040.12-0.16-0.04-0.290.000.08-0.250.040.010.000.25-0.120.140.020.07
2Education0.031.000.060.110.18-0.16-0.01-0.04-0.040.050.030.06-0.010.010.10-0.020.10-0.100.10
3Marriage0.010.061.00-0.040.03-0.02-0.030.02-0.060.030.040.020.010.000.000.010.01-0.37-0.14
4Home type0.040.11-0.041.000.20-0.14-0.05-0.16-0.050.010.020.21-0.010.010.05-0.040.040.080.17
5Company0.120.180.030.201.00-0.41-0.16-0.30-0.180.120.020.24-0.020.020.23-0.070.190.040.35
6Pay_method-0.16-0.16-0.02-0.14-0.411.000.110.290.25-0.150.03-0.19-0.010.00-0.320.09-0.230.01-0.26
7Job type-0.04-0.01-0.03-0.05-0.160.111.000.090.060.000.00-0.030.00-0.010.00-0.04-0.040.02-0.05
8Product name-0.29-0.040.02-0.16-0.300.290.091.000.05-0.100.27-0.13-0.02-0.02-0.340.25-0.17-0.13-0.18
9Sales department0.00-0.04-0.06-0.05-0.180.250.060.051.00-0.06-0.31-0.330.000.01-0.090.01-0.110.03-0.08
10Bank0.080.050.030.010.12-0.150.00-0.10-0.061.00-0.020.09-0.010.000.18-0.070.100.000.09
11Family aware-0.250.030.040.020.020.030.000.27-0.31-0.021.000.100.00-0.03-0.260.19-0.05-0.06-0.04
12Pro_id0.040.060.020.210.24-0.19-0.03-0.13-0.330.090.101.000.000.010.09-0.030.080.050.17
13Birth month0.01-0.010.01-0.01-0.02-0.010.00-0.020.00-0.010.000.001.000.020.010.000.00-0.01-0.01
14Birthday0.000.010.000.010.020.00-0.01-0.020.010.00-0.030.010.021.000.010.000.000.020.00
15Inapv_edr0.250.100.000.050.23-0.320.00-0.34-0.090.18-0.260.090.010.011.00-0.200.720.060.19
16Inapv_idr-0.12-0.020.01-0.04-0.070.09-0.040.250.01-0.070.19-0.030.000.00-0.201.000.35-0.09-0.11
17Inapv_tdr0.140.100.010.040.19-0.23-0.04-0.17-0.110.10-0.050.080.000.000.720.351.000.040.14
18Age0.02-0.10-0.370.080.040.010.02-0.130.030.00-0.060.05-0.010.020.06-0.090.041.000.33
19Entry date0.070.10-0.140.170.35-0.26-0.05-0.18-0.080.09-0.040.17-0.010.000.19-0.110.140.331.00