Data Mining Technique Clustering on Bank Data Set by using rapid Miner.X means techniques

1. CLUSTERING
Presented By:
Punit Kishore
Arbind Kumar

2. Agenda
•
•
•
•
•
•
•
Introduction
Business problem
Analytical problem
Analytical solution
Business solution
Futuristic standpoint
Glimpse to process

3. Introduction
•
•
•
•
XYZ is Leading Non banking finance company
Market Share is 40% of commercial vehicle finance in India
600 branches all over India.
Deals with different commercial vehicle
 Small goods Vehicle (4 wheeler only)
 Light goods Vehicle (6 wheeler Only).
 Heavy goods Vehicle (Above 6 wheeler only)
 Passenger (Commercial only)
 Farm equipment

4. Business Problem
Due to stagnation in the market of Commercial vehicle and
seasonal effect:
 Problem 1 - Whom to be focused for Top-up loans/Different
Promotional Schemes.
 Problem 2 - Whom to be focused for NPA collection/
Refinancing

5. Analytical Problem
•
•
•
Portfolio of 1092 customers of the Single Branch.
Data contains total attributes of 21
Used Attribute: 8
1) Gross Demand
2) Gross Collection
3) Arrears
4) Arrears Month
5) Advance Amount
6) Agreement Value
7) Settlement figure
8) Loan no

6. Analytical Solution
• Group the Customer by using the Data mining
Technique - called Clustering.
• Tool Used - Rapid Miner
• Clustering Technique - X means.
• Execution of data in Rapid Miner by using Optimization
Process to define the number of clusters and than
clustering with X-mean technique
• No of Clusters formed - 4

7. Analytical Solution
ATTRIBUTES
ADVANCE AMOUNT
AG VALUE
MONTH TBC
GROSS_DEMAND
GROSS COLL
ARREARS
ARREARS MONTHS
SETTLEMENT FIGURE
CLUSTER 0 CLUSTER 1
313878
133241
460158
192076
10322
5480
199968
71292
151388
53064
22973
10616
7
2
287611
120942
CLUSTER 2
755663
1194406
12915
1027523
370450
279245
45
1763839
CLUSTER 3
610505
916074
17391
316964
213065
39026
8
621170

8. Analytical Solution
14000000
40000000
35000000
30000000
25000000
20000000
15000000
10000000
5000000
0
12000000
10000000
gross collection
cluster_2 HGVCVMH
8000000
6000000
cluster_2
LGVIGVMGV
4000000
2000000
M
I
V
G
L
M
C
V
G
H
R
G
S
A
P
V
G
S
gross demand
0
cluster_3
1
40000000
35000000
30000000
25000000
20000000
15000000
10000000
5000000
0
M
C
V
G
H
M
I
G
L
V
P
Q
E
V
M
R
F
R
G
S
A
P
D
L
O
G
P
V
G
S
gross
demand
gross
collection
cluster_1
2

9. Analytical Solution
• Cluster 0
 The average loan amount in this set is 3.5 to 4 lakhs
 Average preferable Emi is Rs 10132
 Average collection is 75%
 Comprised of Passenger, Small Goods Vehicle and Light
Goods Vehicle
• Cluster 1
 The average loan amount in this set is 1.5 lakhs
 Average preferable Emi is Rs 5438
 Average collection is 82%
 Mainly Passenger and Small Goods Vehicle

10. Analytical Solution
• Cluster 2
 The average loan amount in this set is 7.55 lakh
 Average preferable Emi is Rs 12915
 Average collection is 37%
 Comprised of Light Goods Vehicle and Heavy Goods
Vehicle
•
Cluster 3
 The average loan amount in this set is 6.10 lakh
 Average preferable Emi is Rs 17391.
 Average collection is 55%.
 Light Goods Vehicle, Heavy Goods Vehicle.

11. Analytical Solution
Cluster 1
Cluster 2

12. Business Solution
• Cluster 1: It contains 499 customers which should be
focused for top up loans/promotional offers- like credit
card, tyre loan, engine loan, power loan. Cluster 0 can
also be focused for the above but it should be optional
call by the Manager.
• Cluster 2: The data here is having the average arrears
of 45 months, which should be handed over to collection
agent and must be forwarded for legal processing.
Similarly in Cluster 3, the average loan amount is high
and are in arrears so executives can be improvised to
maintain the portfolio.

13. Futuristic Standpoint
Your text
here
3
2
If successfully executed, plan for Pan
India
Targeting the Cluster 2 to improve the NPA ratio
1
Targeting the Cluster 1 for Business Capitalization

14. Glimpse To Process
Cluster Model
Cluster 0: 361 items
Cluster 1: 499 items
Cluster 2: 22 items
Cluster 3: 210 items
Total number of items: 1092

15. THANK YOU