Financial Evaluation
 -A Comparison of Old data with new
               data
Example: Two Wheelers
CURRENT STATE                                  Non-Defaulters                      Gain on interest
...
Example: Personal Computers
CURRENT STATE                                  Non-Defaulters                      Gain on int...
Example: Consumer Durables
CURRENT STATE                                  Non-Defaulters                      Gain on inte...
Example: Two Wheelers(New Data)
CURRENT STATE                                  Non-Defaulters                      Gain on...
Example: Consumer Durables(New Data)
CURRENT STATE                                  Non-Defaulters                      Ga...
Example: Personal Comp(New Data)
CURRENT STATE                                  Non-Defaulters                      Gain o...
Example: Two Wheelers(Merged Data)
CURRENT STATE                                  Non-Defaulters                      Gain...
Example: Consumer Durables(Merged Data)
CURRENT STATE                                  Non-Defaulters                     ...
Example: Personal Comp(Merged Data)
CURRENT STATE                                  Non-Defaulters                      Gai...
Comparison over different Product
                Segments
   Product          Previous Data        New Data        Merged...
Upcoming SlideShare
Loading in...5
×

Financial Evaluation

95

Published on

Published in: Business, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
95
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
5
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Financial Evaluation

  1. 1. Financial Evaluation -A Comparison of Old data with new data
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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%
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×