1) The document discusses using k-means clustering to segment an insurance company's business into different coverage products to maximize profits. 2) Five clusters were identified based on variables like claim amount, coverage, income, and claim type. Cluster 2 had the highest claim to coverage ratio but also higher average incomes. 3) Cluster 2 only contained fire and contamination claim types, so the company could focus on increasing other insurance coverage offerings to this segment.

1. CLUSTERING FOR A
INSURANCE
COMPANY
Presented by : Ashish Ranjan
Vaibhav Jain

2. Objective of the Study
O To Maximize the company’s profit by
segmenting the business according to the
coverage products.
O To estimate the claim/coverage ratio for
each segment & focus on reducing the
claims received & increase the customer
coverage base.

3. Data Variables Explained
O Claim Id
O Incident Date
O Claim Type : 1. Wind/Hail 2. Water Damage
O
O
O
O
O
3. Fire/Smoke 4.Contamination
5. Theft/Vandalism
Policy Id
Coverage
Income(in Thousands)
Town size
Others Variables

4. Data Snapshot

5. Data Snapshot

6. Rapid Miner

7. K-Means Clustering

8. Clustering Analysis
Attributes
cluster_0
cluster_1
cluster_2
cluster_3
cluster_4
Claim Amount
32.13595628
646.7065116
429.937594
67.60462253
124.0138065
Coverage
136.2993755
1453.186047
588.8721805
402.1022067
1021.9
Income
46.86104606
199.0813953
109.7518797
83.51800232
134.5645161
Members
2562
86
133
861
310
CLAIM/COVERAGE(%)
23.57747874
44.50266455
73.01034219
16.8127957
12.13561077
The claim/coverage % is highest for Cluster 2 i.e. 73% and the
members contributing towards the same are also less. But the
average income of the members are significantly high.

9. Clustering Analysis
Types of Claims
cluster
1
2
3
4
5
Grand Total
cluster_0
630
389
589
227
727
2562
cluster_1
10
6
46
19
5
86
cluster_2
86
47
cluster_3
228
130
163
70
270
861
cluster_4
95
52
35
15
113
310
Grand Total
963
577
919
378
1115
3952
133
Cluster 2 contains only claim types 3 (Fire) & 4
(Contamination). Hence, company should focus on increasing
their portfolio bucket by offering other insurance coverage's i.e.
1 (Wind/Hail), 2 (Water Damage) & 5 (Theft) .

10. Clustering Analysis
Cluster
>2.5 L (1)
50TH-2.499L(2)
10TH49.99TH(3)
2.5TH9.999TH(4)
<2.5TH(5)
Grand Total
cluster_0
668
623
518
435
318
2562
cluster_1
20
21
17
18
10
86
cluster_2
30
28
25
33
17
133
cluster_3
219
208
165
178
91
861
cluster_4
85
70
56
67
32
310
Grand Total
1022
950
781
731
468
3952

11. Recommendations
O On the basis of analysis, we recommend to
the company to concentrate on clusters
where the average income is high &
penetration of different types of insurance
products are less.
O Overall, by reducing the claim/coverage
ratio & increasing customer base, will lead
to higher profitability for the company.

12. THANK YOU