Mr. Prasad Lingam
Second Year, Symbiosis Institute of Geoinformatics
Address: C/o SIMS ,Second Floor, Range Hills Corner, Kirkee Cantt., Bhosale Nagar Pune 411020
Ms. Dimpy Arora
First Year, Symbiosis Institute of Geoinformatics
Address: C/o SIMS ,Second Floor, Range Hills Corner, Kirkee Cantt., Bhosale Nagar Pune 411020
In this race for survival of the fittest and increasing mental anxiety, a human being tends to overlook some small yet important things, which later on prove to be the reasons for resentment with regards to lives of him and others. In time like these, Insurance is the only ray of hope for him to regain his energy from the setback.
This rather assuring aspect is no doubt acting as a major business strategy for insurance agencies, at the same time unfortunate events are a setback for them. Each year, the insurance companies have to pay out huge amounts after any catastrophic events, and consequently, their profit margins are affected significantly.
GIS as a technology has the ability to handle and fiddle with volumes of data that is otherwise very cumbersome. The flexibility of this ingenious technology facilitates query and analysis that helps in Decision Support for the authorities. This can be used for deciding market strategy as well as designing an action plans during a sudden collapse or resentful payment for a catastrophe. Hence GIS acts as a double-edged sword that can whiz through both the enigmatic and treacherous sides of this business.
In this paper, an attempt has been made to analyse the insurance against housebreak thefts on a GIS platform. The area of study was Shivajinagar in Pune district. The concerned insurance agency wanted to assess the pros and cons of providing insurance to the residents of that area, by analyzing the susceptibility to house break thefts.
Hence, a thorough study with visual representation of the risks associated with such thefts was quite important in this scenario.
This paper will present an overview of how the GIS software applications can be used for effective decision making by the insurance industry. The results obtained helped the company in deciding the market potential and risk in that particular area, facilitating in efficient marketing strategies thus emerging as one of the best customer support agencies in the insurance sector.
Insurance is the only way in which a common man can regain from his losses, due to unforeseen conditions or unpredictable conditions. Insurance agencies take the risk on themselves at a price that would be affordable to the common man. In this event the insurers take care not to end up in paying the losses from their own pockets. But sometimes this results into extremes. It so happens that the companies loose either their customers with a fear from getting stuck in claims, or they find themselves paying some amount from their own pockets. In India, according to the insurance methods and types, very few companies provide insurance against burglary.
GIS has developed into a very reliable decision making tool. It not only provides easy data handling for colossal yet unsystematic data but also facilitates Decision making at the managerial front. GIS, coupled with spatial analytical tools, offers an ideal research environment for processing, analyzing, and modeling housing, mortgage and insurance data sets. GIS offers powerful data mapping and visualization functionality to facilitate spatial explorations of the data. It also allows data from multiple sources and disparate formats to be integrated. Its powerful spatial querying and overlay capabilities greatly facilitate the organization and management of data sets to fit research needs. A combination of all the above
would provide some very effective tools to the insurance industry
In short insurance companies can use GIS to
General Layout of Insurances
- Demarcate areas of peak loss potential.
- Map historic patterns of claims to understand the true spatial distribution of risk.
- Segment high-risk policies by geographic sales region and territory.
- Allocate various premium rates, as per the location of property in risk potential zone.
- Map areas of potential customers.
- Aggregate the possible loss and provide backup plans.
1.1a Life Insurance
Its is taken care of by the Life Insurance Corporation LIC of India
1.1b General Insurance
There are many policies covered under the general insurance category. Some of the policies under this category are:
The policy is designed to cover the various risks under a single policy. It provides protection for property and interest of the insured and family.
It provides cover, which takes care of medical expenses following hospitalization from sudden illness or accident.
Personal Accident Insurance:
This insurance policy provides compensation for loss of life or injury (partial or permanent) caused by an accident.
The policy covers the insured against various eventualities while traveling abroad.
This policy indemnifies the Directors or Officers or other professionals against loss arising from claims made against them by reason of any wrongful Act in their Official capacity.
Motor Vehicles Act states that every motor vehicle plying on the road has to be insured, with at least Liability only policy.
Insurance against selected items f the house can be obtained, these items donot include cash inside the vault etc. items which are continuously moving.
1.2 Problem Statement
The insurance is provided to the customers without any prior checks and analysis. The lack of software tools, limited availability of accurate and comprehensive information on residential properties and neighborhoods, and lack of research computing environments to facilitate the geoprocessing needs of spatial data have further hindered the spatial treatment of insurance market. This resulted in a great deal of effort during claims processing and approval in order to restrict the losses to the company. Even then they some times have to face consequences of payment towards claims thereby affecting the company profit.
The company is required to assess all the pros and cons prior to issuing any insurance policy to the customers. Once this analysis is done by the insurers the policy is issued to customers depending on the ratings of prone, more prone and less prone areas.
In this paper an attempt has been made to provide the insurers a visual component to the risk zones of the area, so that any decision with respect to the approval and sanctioning of a particular premium to a customer is in the favor of the insurer and thus the customer.
1.4 Data Required
Map of scale 1:10,000- This contained the major roads of the area, the individual plots and the locations of major chowks. It was updated in the year 2001
Insurance records, obtained from the insurance company – This data was present in the hard copy format, which was duplicated and fed into the database. Records matching the Shivaji Nagar area only were included. (Company Name cannot be disclosed due to non disclosure policy, in the interest of the company marketing strategy)
Theft records (FIR) obtained from the area police station – Records of the year 2003 and 2004, which were registered under the HBT (House Break Thefts – House/ Office) were considered.
Plot details (plot numbers and house numbers) and ownership records obtained from the PMC – This data was used to develop an attribute of the plot numbers of the various land parcels in the area. This would be a method to locate the addresses of the customers.
1.5 Software Used
- ERDAS Imagine 8.7
- Arc GIS 9
- Visual Basic 6 compatible ESRI Mapobjects2.2 version
- Geo referencing
The map was geo-referenced using the points obtained using the GPS receiver – Garmin eTrex Vista. Specified accuracy – 10 meters
The geo-referenced map was further digitized and various layers of roads, slums, plots, wards, and hotspots were added.
ROADS: This layer was chosen to locate the hotspots with respect to the main roads so as to provide an entry and exit point to the burglar.
SLUMS: In the event of a visual analysis performed during the digitization process, it was observed that concentration of hotspots in the area was centered near a slum. Hence this layer was chosen to locate the hotspots within the proximity of the slums.
WARDS: This layer was chosen to incorporate all the major attributes included in the census report 2001. It was proposed to include these facts in the spatial modeling in order to derive precise results.
PLOTS: This layer defined the distribution of the plots as per the development plan procured from the government. It showed various plot numbers which are still referred to as the postal addresses. This would help in easy location of a particular customer.
The geographic location of a plot determines access to neighbors and neighborhood characteristics. Mortgage lenders and insurers know that the geographic location of the property that secures a loan is a major determinant of their credit risk exposure.
HOTSPOTS: This layer was chosen to locate the areas of occurrences which were labeled as hotspots. These are the areas where the burglary occurred.
The purpose of data modeling is to bring about the design of a database which performs efficiently; contains correct information (and which makes the entry of incorrect data as difficult as possible); whose logical structure is natural enough to be understood by users; and is as easy as possible to maintain and extend.
Spatial database systems provide the underlying database technology for Geographic
Information Systems and other applications. Modeling spatial objects and their operations in spatial databases is a relatively new research area. GIS facilitates the organization and management of geographic data, and also enables researchers to take full advantage of locational information contained in these databases to support the application of spatial statistical and spatial econometric tools. The combination of GIS research infrastructure and recent advances in spatial research thus offer tremendous opportunities for investigating the neighborhood context in insurance market research.
It is thus necessary to examine both the modeling and empirical concerns of neighborhood as part of the insurance approval and premium decisions.
Various attributes of the layers used were incorporated inside the model builder to develop a model that would divide the area into three major zones of prone, more prone and less prone. This would help the company in deciding the premium rates for the individuals located in the three zones.
The work is not intended towards advising the company on changing their premium policies, but concentrates on adopting GIS as tool in analyzing the pros and cons of providing insurance to a customer acting as a decision making tool to allocate the right premium to the client. Thereby save the company from running into losses and safeguard the customer’s interests.
After spatial data generation it was found that the concentration of burglaries was denser in the area as shown in the circle of Figure1.
In order to visualize the relationship between the spatial entities, spatial modeling was performed, the results of which were shown using map objects so as to provide a better user interface to the users.
The final GUI that is the last stage of the project is under construction.
Map objects: It is a software with customization capabilities that enables the user to view the GIS layers without the actual software support, thus reducing the cost of buying the whole software.
We are using a VB6 compatible ESRI Mapobjects2.2 version to enable the user to run queries and visualize results and thus facilitate planning. The codes were an inspiration from http://www.support.esri.com. The user will not be able to perform spatial analysis but in fact use the results of the analysis in the modules to support decision making.
The user will just have to input the plot number; accordingly the module will check the proximity and containment of the plot in a particular risk zone. The premium type as divided pertaining to the risk zone (prone, more prone and less prone) is indicated. This will save manual work of visiting the site, maintenance and searching through the records of thefts and claims, in order to approve the premium type to the customer.
1.7 Conclusions & Recommendations
1. Integration of data, both from the police department and the insurance agencies should be integrated in order to keep a track of both the risk zones (for insurance agencies) and for developing a strategy based on the risk zones for law enforcement thus safeguarding the interests of the society.
2. GIS should be introduced as a tool in the insurance sector, so as to minimize the interoperability of data and aiding in decision making. The benefits of this tool should be clearly made aware of among the insurers.
3. A model should be incorporated to asses the risk zones and hence helping in better analysis and decision making by the insurer. The premium thereby should be decided depending on the results obtained from the analysis.