1. Financial Evaluation
-A Comparison of Old data with new
data

2. Example: Two Wheelers
CURRENT STATE Non-Defaulters Gain on interest
55,17,93,663 4,32,11,827
Net gain
Total loan granted : 63,75,18,692 43,66,701
Defaulters Loss on defaults
Return = 0.68%
8,57,25,029 3,88,45,126
USING MODEL
True non-defaulters Gain on interest
40,10,39497 1,85,17474
Total loan granted Net gain
42,20,62,116 99,98319
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 2.37%
2,10,22619 85,19155
APPLICANTS
Sample size: 22175
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 21,54,56,576 NONDEF NONDEF
15037 4527
Undisbursed loan amount 19564 2611 TRUE DEF 721 1890

3. Example: Personal Computers
CURRENT STATE Non-Defaulters Gain on interest
44,71,72,420 3,35,65,029
Net gain
Total loan granted : 48,12,11,988 2,23,41,740
Defaulters Loss on defaults
Return = 4.64%
3,40,39,568 1,12,23,289
USING MODEL
True non-defaulters Gain on interest
29,27,51,911 1,37,98,652
Total loan granted Net gain
30,12,47,545 1,09,21,795
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 3.62%
84,95,634 28,76,857
APPLICANTS
Sample size: 16004
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 17,99,64,443 NONDEF NONDEF
9562 5314
Undisbursed loan amount 14876 1128 TRUE DEF 270 858

4. Example: Consumer Durables
CURRENT STATE Non-Defaulters Gain on interest
94,72,91,732 2,72,72,028
Net gain
Total loan granted : 99,98,02,695 67,47,544
Defaulters Loss on defaults
Return = 0.67%
5,25,10,963 2,05,24,484
USING MODEL
True non-defaulters Gain on interest
58,52,38,652 1,54,63,255
Total loan granted Net gain
60,71,02,196 70,72,918
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 1.165%
2,18,63,544 83,90,337
APPLICANTS
Sample size: 69317
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 39,27,00,499 NONDEF NONDEF
40094 25620
Undisbursed loan amount 65714 3603 TRUE DEF 1486 2117

5. Example: Two Wheelers(New Data)
CURRENT STATE Non-Defaulters Gain on interest
601459209 87914417
Net gain
Total loan granted : 786282237 6982160
Defaulters Loss on defaults
184823028 80932257 Return = 0.8880 %
USING MODEL
True non-defaulters Gain on interest
388678119 37025071
Total loan granted Net gain
432784406 20020403
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 4.6260 %
44106287 17004668
APPLICANTS
Sample size: 24234
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 21,54,56,576 NONDEF NONDEF
12885 6055
Undisbursed loan amount 18940 5294 TRUE DEF 1362 3932

6. Example: Consumer Durables(New Data)
CURRENT STATE Non-Defaulters Gain on interest
680092801 19910129
Net gain
Total loan granted : 726018235 260253
Defaulters Loss on defaults
45925434 19649876 Return = 0.0358 %
USING MODEL
True non-defaulters Gain on interest
393716748 10587710
Total loan granted Net gain
411852797 2727687
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 0.6623 %
18136049 7860023
APPLICANTS
Sample size: 50441
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 314165438 NONDEF NONDEF
27233 20064
Undisbursed loan amount 47297 3144 TRUE DEF 1276 1868

7. Example: Personal Comp(New Data)
CURRENT STATE Non-Defaulters Gain on interest
490015269 44041499
Net gain
Total loan granted : 538900888 26378001
Defaulters Loss on defaults
48885619 17663498 Return = 4.8948 %
USING MODEL
True non-defaulters Gain on interest
315943875 18268223
Total loan granted Net gain
329895002 12799299
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 3.8798 %
13951127 5468924
APPLICANTS
Sample size: 17649
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 209005886 NONDEF NONDEF
10170 5905
Undisbursed loan amount 16075 1574 TRUE DEF 444 1130

8. Example: Two Wheelers(Merged Data)
CURRENT STATE Non-Defaulters Gain on interest
950077556 109332074
Net gain
Total loan granted : 1.1748e+009 7733639
Defaulters Loss on defaults
224720399 101598435 Return = 0.6583%
USING MODEL
True non-defaulters Gain on interest
620546596 38186654
Total loan granted Net gain
661921412 20987314
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 3.1707%
41374816 17199340
APPLICANTS
Sample size: 38275
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 512876543 NONDEF NONDEF
22160 9602
Undisbursed loan amount 31762 6513 TRUE DEF 1309 5204

9. Example: Consumer Durables(Merged Data)
CURRENT STATE Non-Defaulters Gain on interest
1.2791e+009 35317232
Net gain
Total loan granted : 1.3502e+009 5078212
Defaulters Loss on defaults
71081181 30239020 Return = 0.3761%
USING MODEL
True non-defaulters Gain on interest
796498724 20047855
Total loan granted Net gain
827112186 7129102
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 0.8619%
30613462 12918753
APPLICANTS
Sample size: 93572
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 523117298 NONDEF NONDEF
54677 34028
Undisbursed loan amount 88705 4867 TRUE DEF 2095 2772

10. Example: Personal Comp(Merged Data)
CURRENT STATE Non-Defaulters Gain on interest
727340241 59708021
Net gain
Total loan granted : 790246532 37454983
Defaulters Loss on defaults
62906291 22253038 Return = 4.7397 %
USING MODEL
True non-defaulters Gain on interest
486346652 25420033
Total loan granted Net gain
504692651 18558671
PREDICTED
NON-DEFAULTERS True defaulters Loss on defaults Return = 3.6772%
18345999 6861362
APPLICANTS
Sample size: 26073
MODEL PREDICTED PREDICTED
Assumed rate of risk free interest: 0
RESULTS NONDEF DEF
PREDICTED TRUE
TRUE DEF TRUE
DEFAULTERS 285553881 NONDEF NONDEF
15856 8140
Undisbursed loan amount 23996 2077 TRUE DEF 597 1480

11. Comparison over different Product
Segments
Product Previous Data New Data Merged Data
Segment
Without Using Without Using Without Using
Model Model Model Model Model Model
Two Wheeler 0.68% 2.37% 0.89% 4.63% 0.66% 3.17%
Consumer Durables 0.67% 1.16 0.036% 0.66% 0.38% 0.86%
Personal Computer 4.64% 3.62% 4.90% 3.88% 4.73% 3.68%