Risk analysis tools providing various natural disasters

Moldova should have , as the U.S. and Europe , a law on flood insurance .
Data storage is done in an arbitrary manner , without a well-tuned system .
Whether it comes true theory of " global warming " or that have strengthened
natural disasters lately, but their frequency in recent
four years ( 2007 drought , floods in 2008 and 2010) should
highlight the imperative search for complex solutions
protection against such risks. Thus, one of the
solutions can provide assurance that already for more than a few
decades , is successfully implemented in developed countries. For example, the
in U.S. law on flood insurance was adopted in
, 1968. A similar system already exists and social protection in the EU and
Romania recently introduced mandatory household insurance
against natural disasters by Law No. 260 of 2008.

In this context, I would like to go into more technical question that you
institutional and technological capacities of the insurance industry
Moldova to be at the height of international standards
in risk assessment data . This is primarily the
implementation of actuarial risk assessment models based on
geo- information technologies which are successfully applied much
while in countries with developed insurance industry and of course
reinsurers that provide protection in this regard , such as Munich Re
and others .

Geographic Information System (GIS ) - an important factor in the insurance industry in Moldova

Given the relative newness of the phenomenon of geo- information system in
our country and for the insurance sector , which has not yet
penetrated , being a stranger , I would start with giving a simple definition of the
what might be called geographic information system . It is well known that
that most of the information contained geographical aspects ( location )
so it appeared the need to process and extract
important elements for making decisions.
Finally, geographic information system can be described as totally bases
data structured in a special way in order to optimize the
analysis , review and decision making.

The purpose of applying

It is common practice to insurance companies to collect information
about the location of an object of insurance before determining Quote
and issues the insurance policy. This refers to a series of classes
insurance such as fire insurance , flood , earthquake and
other natural disaster , property insurance . Even policies
compulsory auto liability Affairs ( RCA) containing
address both the insurer that issued the policy and carries responsibility
respectively and address of the insured , and useful information
for all kinds of analysis from the point of view of insurers
( sales analysis , customer dispersion , etc. . ) .

Already regulations of the National Commission on Financial Market
establishing basic insurance premium and the coefficient of
correction to the insurance compulsory auto liability
internal and external , approved by Decision no. 53/5 of 31.10.2008 ,
provide application rectification coefficient determined by
for use within a vehicle , respectively , using the
geographic information .
However, the use of information collected leaves much to be desired, since that storage
data is done in an arbitrary manner , without a well-tuned system .
On the other hand , there is a vital necessity for any time
management , actuarial companies can access , process and analyze
information on clusters of customers and items provided (ie
concentration risk ) or on acceptable levels exceeding
underwriting risks data . For example, in the U.S. there is
maintain geographic database that provides risk view
aggregates and the damage caused in the territorial aspect
whole country. Similar information systems are implemented in
most EU Member States .

It has already been spread phenomenon
creation of geo- information database that includes risk
covered and allow modeling of the effects of accumulation of risk
for greater accuracy in determining the premium.
This allows the location of Portfolio subscribed and the
damage occurred , describing their social and economic impact .
As a result, such a system more accurately reflect
likelihood of risk occurrence , respectively , establishes the
for determining an optimal insurance premiums .

Also most systems have major reinsurers , which already
many years perfecting their risk assessment methodology
by introducing GIS . A classic of its kind is the
The NATHAN of Munich Re .

The latter serves approximately 5,000 insurance companies in 160 countries worldwide.

This system has a special category of risk classification in terms of
geographically. From it we can see the degree of exposure to various
natural disasters and the percentage of territory seized by them. with
However , the exact location of the part of the territory is at risk
NATHAN system accurately determined only in America
North ( U.S. and Canada) and in about 20 countries in the EU , where such bases
The data were developed . Even if this percentage is determined and
Moldova , geographical location of the risk remains
responsibility of local companies. For example , to analyze the
risk of flooding and freezing , great importance has model
elevation of the terrain with high precision .

Needless mention
that with increasing degree of accuracy of location risk
will be other requirements for the negotiation of more favorable conditions for
reassurance and in particular, such companies as Re Munich , having
implement and develop risk assessment models based on
geographic information systems .

Agricultural risks

A special geographical information systems have in
providing agricultural goods , since the location of land
insured and their overlap with the types of risks spread
the area and to view crop productivity in
geographically and monitoring the evolution in time and space of these
indicators. Such databases are already compiled and maintained in
Moldova various institutions and private companies , which are
implemented in an information system may present tools
effective activity of insurance companies that underwrite risks
farm . A more advanced stage in the use of these data is
composition models actuarial training in insurance premiums
dependent interaction of multiple factors , such as structure
soil temperature regime , the history of cultivation of various crops
secured area , and other parameters. The model presents a
managerial aspect of the implementation of geographic information systems ,
emphasizing efficiency playback information , and a look
territory, both are possible within the GIS , the latest offering
possibility of a long narrow understanding of statistical information
presented , which can often be obtained if the presentation
information in tabular form .

Marketing and sales network development

A geographic information system is also a perfect tool
for operational integration of activities of different subdivisions
of insurance companies , including the organization of
marketing and sales promotion .
Since viewing
portfolio risk distribution depending on the number of
sure each class, determine the rental optimal
for location or branch outlets and to
segmentation by various criteria , such as profitability ,
customer ability to pay , age etc .
experience countries
developed shows that managers , actuaries and companies underwriterii
perceive insurance as geographical information system integration
natural one information system and business processes
existing and that allow greater transparency in administration
claims and underwriting risks.
Considering all specialization
deeper now taking place in the various subdivisions of
insurance companies with their development and increasing
their capacity , it outlines more acute need to find a
solution that would unite the efforts of all segments and provide a look
Overall the portfolio entirely and evolution over time and
space enterprise activity .
The main benefits of a
Such information system is materialized by an understanding
deep by senior managers of the business processes in
whole and their dynamic development , a risk management
effective control over its accumulation , an information system
closer to reality , based on geo- informational processes .

Eugene Hristev