Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory75.8 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description2023년도 자료입니다. 국민연금 징수종별 기금입금통계자료로 수납건수, 수납금액 등의 자료를 제공합니다.연간으로 업데이트 할 예정입니다.업무에 참고하시기 바랍니다.
Author국민연금공단
URLhttps://www.data.go.kr/data/3046069/fileData.do

Alerts

수납건수 is highly overall correlated with 수납금액 and 1 other fieldsHigh correlation
수납금액 is highly overall correlated with 수납건수 and 2 other fieldsHigh correlation
보험료 is highly overall correlated with 수납건수 and 2 other fieldsHigh correlation
연체금 is highly overall correlated with 수납금액 and 1 other fieldsHigh correlation
차감건수 is highly overall correlated with 차감금액High correlation
차감금액 is highly overall correlated with 차감건수High correlation
수납건수 has 3 (13.6%) zerosZeros
수납금액 has 3 (13.6%) zerosZeros
보험료 has 3 (13.6%) zerosZeros
연체금 has 16 (72.7%) zerosZeros
차감건수 has 13 (59.1%) zerosZeros
차감금액 has 13 (59.1%) zerosZeros

Reproduction

Analysis started2024-03-14 10:44:57.699053
Analysis finished2024-03-14 10:45:07.102634
Duration9.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size304.0 B
2023-12-31
11 
2023-06-30
11 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-31
2nd row2023-12-31
3rd row2023-12-31
4th row2023-12-31
5th row2023-12-31

Common Values

ValueCountFrequency (%)
2023-12-31 11
50.0%
2023-06-30 11
50.0%

Length

2024-03-14T19:45:07.299835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:45:07.615955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-31 11
50.0%
2023-06-30 11
50.0%

구분
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-14T19:45:08.262102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.4545455
Min length2

Characters and Unicode

Total characters76
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row직접수납
2nd row사업장
3rd row농어민
4th row자영자
5th row임의
ValueCountFrequency (%)
직접수납 2
9.1%
사업장 2
9.1%
농어민 2
9.1%
자영자 2
9.1%
임의 2
9.1%
임계 2
9.1%
반납금 2
9.1%
추납금 2
9.1%
부당이득환수 2
9.1%
실업크레딧 2
9.1%
2024-03-14T19:45:09.088731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 6
 
7.9%
납 6
 
7.9%
금 4
 
5.3%
수 4
 
5.3%
업 4
 
5.3%
이 4
 
5.3%
임 4
 
5.3%
당 2
 
2.6%
득 2
 
2.6%
환 2
 
2.6%
Other values (19) 38
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 6
 
7.9%
납 6
 
7.9%
금 4
 
5.3%
수 4
 
5.3%
업 4
 
5.3%
이 4
 
5.3%
임 4
 
5.3%
당 2
 
2.6%
득 2
 
2.6%
환 2
 
2.6%
Other values (19) 38
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 6
 
7.9%
납 6
 
7.9%
금 4
 
5.3%
수 4
 
5.3%
업 4
 
5.3%
이 4
 
5.3%
임 4
 
5.3%
당 2
 
2.6%
득 2
 
2.6%
환 2
 
2.6%
Other values (19) 38
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 6
 
7.9%
납 6
 
7.9%
금 4
 
5.3%
수 4
 
5.3%
업 4
 
5.3%
이 4
 
5.3%
임 4
 
5.3%
당 2
 
2.6%
득 2
 
2.6%
환 2
 
2.6%
Other values (19) 38
50.0%

수납건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196752.14
Minimum0
Maximum1049134
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T19:45:09.294976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median4386
Q3150377.75
95-th percentile1020558.5
Maximum1049134
Range1049134
Interquartile range (IQR)150376

Descriptive statistics

Standard deviation365081.42
Coefficient of variation (CV)1.8555398
Kurtosis1.4442934
Mean196752.14
Median Absolute Deviation (MAD)4386
Skewness1.75689
Sum4328547
Variance1.3328444 × 1011
MonotonicityNot monotonic
2024-03-14T19:45:09.507163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3
13.6%
0 3
13.6%
6 2
 
9.1%
187524 1
 
4.5%
1027228 1
 
4.5%
786163 1
 
4.5%
3112 1
 
4.5%
57734 1
 
4.5%
41995 1
 
4.5%
8 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0 3
13.6%
1 3
13.6%
4 1
 
4.5%
6 2
9.1%
8 1
 
4.5%
3112 1
 
4.5%
5660 1
 
4.5%
40844 1
 
4.5%
41995 1
 
4.5%
54029 1
 
4.5%
ValueCountFrequency (%)
1049134 1
4.5%
1027228 1
4.5%
893838 1
4.5%
786163 1
4.5%
187524 1
4.5%
181259 1
4.5%
57734 1
4.5%
54029 1
4.5%
41995 1
4.5%
40844 1
4.5%

수납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2302837 × 1010
Minimum0
Maximum2.6590237 × 1011
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T19:45:09.715711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12211367.5
median2.5100508 × 109
Q31.2090004 × 1011
95-th percentile2.559779 × 1011
Maximum2.6590237 × 1011
Range2.6590237 × 1011
Interquartile range (IQR)1.2089783 × 1011

Descriptive statistics

Standard deviation9.0182515 × 1010
Coefficient of variation (CV)1.4474865
Kurtosis0.20632506
Mean6.2302837 × 1010
Median Absolute Deviation (MAD)2.5100508 × 109
Skewness1.2199637
Sum1.3706624 × 1012
Variance8.132886 × 1021
MonotonicityNot monotonic
2024-03-14T19:45:09.932731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 3
 
13.6%
265902370390 1
 
4.5%
6816510 1
 
4.5%
159568774530 1
 
4.5%
13794415080 1
 
4.5%
1120782980 1
 
4.5%
170627231800 1
 
4.5%
86960984910 1
 
4.5%
5359530 1
 
4.5%
10385790 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
0 3
13.6%
112320 1
 
4.5%
173280 1
 
4.5%
1161980 1
 
4.5%
5359530 1
 
4.5%
6816510 1
 
4.5%
10385790 1
 
4.5%
14515600 1
 
4.5%
1120782980 1
 
4.5%
3899318550 1
 
4.5%
ValueCountFrequency (%)
265902370390 1
4.5%
260317935840 1
4.5%
173517144200 1
4.5%
170627231800 1
4.5%
159568774530 1
4.5%
132213063130 1
4.5%
86960984910 1
4.5%
86777818950 1
4.5%
15924055170 1
4.5%
13794415080 1
4.5%

보험료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.685742 × 1011
Minimum0
Maximum2.5996609 × 1012
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T19:45:10.245144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12211367.5
median2.5100508 × 109
Q31.2083918 × 1011
95-th percentile2.6079013 × 1011
Maximum2.5996609 × 1012
Range2.5996609 × 1012
Interquartile range (IQR)1.2083697 × 1011

Descriptive statistics

Standard deviation5.4863517 × 1011
Coefficient of variation (CV)3.2545619
Kurtosis20.972439
Mean1.685742 × 1011
Median Absolute Deviation (MAD)2.5100508 × 109
Skewness4.5369881
Sum3.7086324 × 1012
Variance3.0100055 × 1023
MonotonicityNot monotonic
2024-03-14T19:45:10.470755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 3
 
13.6%
265391837240 1
 
4.5%
6816510 1
 
4.5%
159568774530 1
 
4.5%
13794415080 1
 
4.5%
1120782980 1
 
4.5%
170518818450 1
 
4.5%
86717545660 1
 
4.5%
5359530 1
 
4.5%
10385790 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
0 3
13.6%
112320 1
 
4.5%
173280 1
 
4.5%
1161980 1
 
4.5%
5359530 1
 
4.5%
6816510 1
 
4.5%
10385790 1
 
4.5%
14515600 1
 
4.5%
1120782980 1
 
4.5%
3899318550 1
 
4.5%
ValueCountFrequency (%)
2599660873240 1
4.5%
265391837240 1
4.5%
173357761740 1
4.5%
170518818450 1
4.5%
159568774530 1
4.5%
132213063130 1
4.5%
86717545660 1
4.5%
86426668260 1
4.5%
15924055170 1
4.5%
13794415080 1
4.5%

연체금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78398705
Minimum0
Maximum5.1053315 × 108
Zeros16
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T19:45:10.672172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q381310012
95-th percentile3.518175 × 108
Maximum5.1053315 × 108
Range5.1053315 × 108
Interquartile range (IQR)81310012

Descriptive statistics

Standard deviation1.4943548 × 108
Coefficient of variation (CV)1.9060963
Kurtosis2.5440469
Mean78398705
Median Absolute Deviation (MAD)0
Skewness1.8644658
Sum1.7247715 × 109
Variance2.2330962 × 1016
MonotonicityNot monotonic
2024-03-14T19:45:10.865876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16
72.7%
510533150 1
 
4.5%
351150690 1
 
4.5%
159382460 1
 
4.5%
351852600 1
 
4.5%
243439250 1
 
4.5%
108413350 1
 
4.5%
ValueCountFrequency (%)
0 16
72.7%
108413350 1
 
4.5%
159382460 1
 
4.5%
243439250 1
 
4.5%
351150690 1
 
4.5%
351852600 1
 
4.5%
510533150 1
 
4.5%
ValueCountFrequency (%)
510533150 1
 
4.5%
351852600 1
 
4.5%
351150690 1
 
4.5%
243439250 1
 
4.5%
159382460 1
 
4.5%
108413350 1
 
4.5%
0 16
72.7%

차감건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1363636
Minimum0
Maximum19
Zeros13
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T19:45:11.143213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8.85
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.4325147
Coefficient of variation (CV)2.0747941
Kurtosis10.251387
Mean2.1363636
Median Absolute Deviation (MAD)0
Skewness3.039986
Sum47
Variance19.647186
MonotonicityNot monotonic
2024-03-14T19:45:11.511684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 13
59.1%
1 2
 
9.1%
2 2
 
9.1%
9 1
 
4.5%
4 1
 
4.5%
19 1
 
4.5%
6 1
 
4.5%
3 1
 
4.5%
ValueCountFrequency (%)
0 13
59.1%
1 2
 
9.1%
2 2
 
9.1%
3 1
 
4.5%
4 1
 
4.5%
6 1
 
4.5%
9 1
 
4.5%
19 1
 
4.5%
ValueCountFrequency (%)
19 1
 
4.5%
9 1
 
4.5%
6 1
 
4.5%
4 1
 
4.5%
3 1
 
4.5%
2 2
 
9.1%
1 2
 
9.1%
0 13
59.1%

차감금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2212836.4
Minimum-18945830
Maximum0
Zeros13
Zeros (%)59.1%
Negative9
Negative (%)40.9%
Memory size326.0 B
2024-03-14T19:45:11.866970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-18945830
5-th percentile-14308148
Q1-900527.5
median0
Q30
95-th percentile0
Maximum0
Range18945830
Interquartile range (IQR)900527.5

Descriptive statistics

Standard deviation5243684.7
Coefficient of variation (CV)-2.3696667
Kurtosis5.4172959
Mean-2212836.4
Median Absolute Deviation (MAD)0
Skewness-2.5126774
Sum-48682400
Variance2.749623 × 1013
MonotonicityNot monotonic
2024-03-14T19:45:12.240869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 13
59.1%
-1306930 1
 
4.5%
-1161980 1
 
4.5%
-28780 1
 
4.5%
-116170 1
 
4.5%
-18945830 1
 
4.5%
-13130 1
 
4.5%
-14515600 1
 
4.5%
-10366570 1
 
4.5%
-2227410 1
 
4.5%
ValueCountFrequency (%)
-18945830 1
 
4.5%
-14515600 1
 
4.5%
-10366570 1
 
4.5%
-2227410 1
 
4.5%
-1306930 1
 
4.5%
-1161980 1
 
4.5%
-116170 1
 
4.5%
-28780 1
 
4.5%
-13130 1
 
4.5%
0 13
59.1%
ValueCountFrequency (%)
0 13
59.1%
-13130 1
 
4.5%
-28780 1
 
4.5%
-116170 1
 
4.5%
-1161980 1
 
4.5%
-1306930 1
 
4.5%
-2227410 1
 
4.5%
-10366570 1
 
4.5%
-14515600 1
 
4.5%
-18945830 1
 
4.5%

Interactions

2024-03-14T19:45:04.892920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:58.004023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:59.512715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:00.824402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:02.289726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:03.374241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:05.143038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:58.246195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:59.722254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:01.065476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:02.447738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:03.623693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:05.395817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:58.494527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:59.903247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:01.310303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:02.586822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:03.870440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:05.648838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:58.737622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:00.257897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:01.551643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:02.759270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:04.119293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:05.902938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:58.984297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:00.435693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:01.795413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:02.917530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:04.372406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:06.163734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:44:59.245738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:00.590570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:02.058211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:03.117786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:04.629875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:45:12.502661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월구분수납건수수납금액보험료연체금차감건수차감금액
기준년월1.0000.0000.0000.0000.0000.0000.0000.326
구분0.0001.0000.8820.9300.4410.5060.6560.000
수납건수0.0000.8821.0000.8810.6560.0000.3390.000
수납금액0.0000.9300.8811.0000.6560.7530.3650.000
보험료0.0000.4410.6560.6561.0000.9771.0000.000
연체금0.0000.5060.0000.7530.9771.0000.8070.000
차감건수0.0000.6560.3390.3651.0000.8071.0000.623
차감금액0.3260.0000.0000.0000.0000.0000.6231.000
2024-03-14T19:45:12.986655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수납건수수납금액보험료연체금차감건수차감금액기준년월
수납건수1.0000.8860.8850.392-0.1000.1890.000
수납금액0.8861.0000.9990.7090.1070.0620.000
보험료0.8850.9991.0000.7080.1090.0560.000
연체금0.3920.7090.7081.0000.274-0.0630.000
차감건수-0.1000.1070.1090.2741.000-0.7920.000
차감금액0.1890.0620.056-0.063-0.7921.0000.183
기준년월0.0000.0000.0000.0000.0000.1831.000

Missing values

2024-03-14T19:45:06.526700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:45:06.944314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기준년월구분수납건수수납금액보험료연체금차감건수차감금액
02023-12-31직접수납1875242659023703902653918372405105331509-1306930
12023-12-31사업장000000
22023-12-31농어민11161980116198001-1161980
32023-12-31자영자417328017328004-28780
42023-12-31임의000000
52023-12-31임계111232011232002-116170
62023-12-31반납금40844867778189508642666826035115069000
72023-12-31추납금5402917351714420017335776174015938246020
82023-12-31부당이득환수566038993185503899318550000
92023-12-31실업크레딧8938381592405517015924055170000
기준년월구분수납건수수납금액보험료연체금차감건수차감금액
122023-06-30사업장16816510681651001-13130
132023-06-30농어민6145156001451560006-14515600
142023-06-30자영자8103857901038579000-10366570
152023-06-30임의000000
162023-06-30임계65359530535953003-2227410
172023-06-30반납금41995869609849108671754566024343925000
182023-06-30추납금5773417062723180017051881845010841335000
192023-06-30부당이득환수311211207829801120782980000
202023-06-30실업크레딧7861631379441508013794415080000
212023-06-30자동이체1027228159568774530159568774530000