Issue 10: July 2014

INSIDE THIS ISSUE

ISM NetISM Net Motor Rating Model

The industry has been anticipating the motor insurance to be de-

tariffed for some time now. This enables the companies to charge pre-

mium based on the individual risk profile.

In light of this, we proceeded to develop the Motor Rating Model to

support the liberalized pricing environment which is one of our core

objectives. The model was developed using industry motor data and a

preview was held on the 27 May 2014, giving the attendees an over-

view on the development, data management and regression model

used.

The Motor Rating Model will be made available for subscription by

this year. The early completion of the model paves the path for com-

panies who wish to strategize the management of your motor portfo-

lio in advance.

For further enquiries pertaining to the Motor Rating Model, please

contact Kenny@ism.net.my.

Motor Rating Model …………………. 1

KMS Enhancements …………………. 2

ABI—Motorcycle Database ………. 2

Industry Insight ………………………… 3

2

Q2 2014 Updates

The Automotive Business Intelligence has

undergone several enhancements since its

introduction. The enhancements include the

provision of residual values for private vehi-

cles from 1995, revision of the data delivery

mechanism, inclusion of residual values of

vehicles in East Malaysia and Grey Imports.

As our efforts to continuously enhance and

expand the service, we will be introducing a

new module namely residual values for mo-

torcycles. This database which dates back to

year 2005 will include cub-motorcycles,

scooters, naked-bikes, sport-bikes, cruisers

and dual sports type motorcycles which are

locally assembled, officially imported and

parallel imports. The residual values will be

updated on a quarterly basis.

If you require more information on this new

service, please contact Prakash at pra-

kash@ism.net.my.

Knowledge Management System (KMS) - Enhancement

Analytics will continue to drive strategies, especially in the insurance and takaful industry. Keeping

this in mind, we continuously enhance the Knowledge Management System to meet your dynamic

needs for analytics.

Recently, we have revamped the motor theft frequency report which are now known as Motor Theft

Statistics. This reports provides you with one-way and two-way analysis in relation to vehicle theft.

Aside to that, you can also get a snapshot on the top 20 models being stolen by vehicle class. The

Motor Theft Statistics are available for all the Motor module subscribers on a complimentary basis.

If you wish to find out more or get a trial ID, please contact Jasvinder at Jasvinder@ism.net.my.

“..one-way and two-way analysis in relation to vehicle theft”.

Automotive Business Intelligence - Motorcycle Database

INDUSTRY INSIGHT

3

The Factors behind the Claims Occurrence in the Policy Lifecycle

Introduction

In the previous issue of Industry Insight, ISM attempted to discover trends that may reveal insights

into the frequency of claims during the lifecycle of a policy. We hypothesized that observing the

claims occurrence patterns during a policy’s lifecycle could exhibit trends which are possibly indica-

tors of elements of fraud or seasonal claims. From the last issue, no firm conclusion was able to be

made, however we have identified a few areas that needed further analysis and these are:-

1. In general, each vehicle class shows a negative correlation between the number of days of policy

cover and the number of claims. More claims are intimated when a policy is new than when a

policy is closer to expiry.

2. In their specific classes; most Motorcycle claims are Theft claims, Private Car claims are mostly

Own Damage claims; and the highest number of Commercial Vehicle claims are Third Party

Property Damage claims.

3. While the vehicle classes generally experience the same trend in claims count with relation to

days of cover, Private Car claims experience an increase during final days of policy cover, i.e.

closer expiry. Further analysis will be required for these claims to discover any other trends and/

or correlating factors.

4. There are three exceptions to the general decreasing trend observable in the data, viz. Private

Car Own Damage claims, Private Car Windscreen claims and Commercial Vehicle Windscreen

claims. The Windscreen claims in both the Private Car and Commercial vehicle classes display

extreme increases as the policy approaches expiry. It will be necessary for us to find other cor-

relating factors. However, these trends exhibit potential to be reasonable indicators of higher

likelihood of fraud.

INDUSTRY INSIGHT

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Figure 1 – Motorcycle Average Claims Count (2009-2011) by Nature of Loss

Figure 2 – Private Car Average Claims Count (2009-2011) by Nature of Loss

INDUSTRY INSIGHT

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Figure 3 – Commercial Vehicle Average Claims Count (2009-2011) by Nature of Loss

In this report, a segmentation analysis will be carried out to identify the characteristics of policyhold-

ers and vehicles during the policy lifecycle. The analyses are segmented by the Age of Insured, Gen-

der, Age of Vehicle and No Claims Discount. The analysis will cover:

Motorcycle Theft Claims

Private Car Own Damage Claims

Private Car Windscreen Claims

We have chosen to segment the analysis by the characteristics of the policyholder in order to scruti-

nize the pre accident factors that would indicate an increased likelihood for the trends observed in

our initial analyses.

INDUSTRY INSIGHT

6

Insurance Exposure

In order to prevent the discrimination by characteristic factors, the exposure of the private car and

motorcycle by risk factors are shown in the following prior to the segmentation analysis.

Figure 4 – Private Car Exposure by Risk Factors

Figure 5 – Motorcycle Exposure by Risk Factors

INDUSTRY INSIGHT

7

Segmentation Analysis

Motorcycle Theft Claims

From the study, the most frequently occurring claims for Motorcycle policy are theft claims. It was

also observed that the general trend of motorcycle claims is sloping downward from left to right,

meaning more claims are intimated when the policy is new than when the policy is closer to the ex-

piry.

It would be quite intuitive to conclude that characteristics of the policyholder should have no direct

relationship to the likelihood of the insured vehicle being stolen. But the fact that the numbers of

theft claims are more than double the number of other claims combined is a serious concern.

Whether or not this high theft count is due to the negligence of riders, the lack of anti-theft devices

installed in motorcycles, or fraudulent claims, if there is a relationship between the theft cases and

characteristics of the policyholder or vehicle, this would provide some insight into the risk being un-

derwritten.

This segmentation analysis aims to identify the group that loses their motorcycle through theft.

Figure 6 – Motorcycle Theft (Age of Insured)

INDUSTRY INSIGHT

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The age proportion that claims for motorcycle theft is broadly similar across the policy period. It also

indicates that the claimants are predominantly young - below the age of 35. There are a significant

number of claims by the less than 17 years old group in the first 15 days from the policy inception

date.

Figure 7 – Motorcycle Theft (Gender)

The motorcycle theft claims by gender analysis also does not suggest any extraordinary trends apart

from there are higher claims by companies. In general, the claims are in relation to the portfolio mix

of motorcycle policies.

Figure 8 – Motorcycle Theft (Age of Vehicle)

INDUSTRY INSIGHT

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For the age of vehicle category, motorcycle thefts are largely thefts of vehicles that are less than 6

years old. This is reasonable as there are a higher number of exposures of motorcycle covers for be-

low 6 years vehicle - 82% of total motorcycle policies. With relation to 1-5 year vehicles, these new

vehicles show a higher claim frequency.

Private Car Own Damage Claims

The trend of claims for private cars is similar to those for motorcycles – higher claims count at the

beginning of the policy period compared to the end of the policy period. There is also a sharp in-

crease in the number of claims close to the expiry of the policy.

The own damage claims of private car which accounts for almost 50% of total claims will be ana-

lysed. The characteristics of the insured and vehicle are presented below.

Figure 9 – Private Car Own Damage (Age of Insured)

INDUSTRY INSIGHT

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For the private car own damage claims, the insured age 26-35 group with a share of 30% is the main

claimant category, followed by the 36-45 group and 46-55 group. The only indicative trend is that

the 17-25 age group has a higher claims frequency compared to the other groups.

Figure 10 – Private Car Own Damage (Gender)

Figure 11 – Private Car Own Damage (Age of Vehicle)

The other two characteristics, i.e. gender and age of vehicle for private care own damage claims do

not show any unusual trend. The claims patterns are broadly in line with the exposures.

INDUSTRY INSIGHT

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Figure 12 – Private Car Own Damage (No Claims Discount)

The claims occurrence during the policy life by NCD status have seen a more own damage claims

coming from 0% NCD, follow by 55% and 25%. This is rather unusual as we would expect the policy-

holders who are enjoying the 55% discount to be more cautious on the road than he/she had been.

Would claiming the damage be more financially viable compared to keeping the discount? A further

study of claims with NCD status against claims severity would need to be carried out on this claims

behavior.

Private Car Windscreen Claims

For windscreen claims for private cars, the number of claims increases as the policy approaches ex-

piry. At its peak, the number of claims is almost three times as many compared to the average

claims count. Similarly, we analyzed the breakdown of the policyholder and vehicle characteristics

to discover factors that correlate with this segment.

Figure 13 – Private Car Windscreen (Age of Insured)

INDUSTRY INSIGHT

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Figure 14 – Private Car Windscreen (Gender)

Figure 15 – Private Car Windscreen (Age of Vehicle)

Figure 16 – Private Car Windscreen (No Claims Discount)

Although the trend of claims by policy period is showing a significant claims experience towards the

end of the policy period, however, the study by characteristic of vehicle and policyholder failed to

identify any perceptible trends.

INDUSTRY INSIGHT

ISM Insurance Services Malaysia Berhad

Unit1-1, Level 1, Tower 9, Avenue 5

Bangsar South, The Horizon

No.8, Jalan Kerinchi

59200 Kuala Lumpur

Phone: +603 2240 9899

Fax: +603 2240 9890

E-mail: info@ism.net.my

13

Conclusion The characteristics with the highest claims experiences are summarized in the following table:

From this study, the frequency of claims during the life cycle re-

veals a very significant trend. It suggests that the characteristics

of vehicle and policyholder have no direct relationship with the

claims experiences.

Does this mean that it is a Malaysian insurance industry practice

that everyone claims at certain time in the policy life cycle? Can

this be categorized as opportunistic claim which the claimant

thinks his/her claims might not be discovered by the insurers?

Could these be considered as fraud cases?

There are many questions to be answered before it can be con-

firmed that a claim is a genuine or fraud case, especially there are

other parties involved in a claims process, for example, third par-

ty service providers, internal claims processor, workshop etc.

There is no analysis for these as no information is available

Variable Private Car Own Damage Motorcycle Theft Private Car Windscreen

1. Age of Insured 26-35 Years <35 Years 26-35 Years

2. Gender Male Male Male

3. Age of Vehicle < 10 Years < 7 Years < 10 Years

4. No Claim Discount 0% 0% 55%