Insurance is such a service, which can extend its scope to every
section of the society. From richest to poorest, from big corporate to
small individual entrepreneur, from space research to agriculture - take
any walk of life, insurance can add lot of values to it by helping in
managing the risk. Formulating strategies for different segments of
society and to meet different risk management needs involve lot of data
acquisition, automation of workflow and intelligent analytics.
Many
mathematical and statistical techniques, which were confined to remain
topics of academic interest only till recently, have started making
increasing contribution in present day services. The reasons being the
availability of high speed computing devices at affordable cost and the
ease, at which large volume of business data can be stored, retrieved
and processed. Software companies have flooded the automation market
with various products and tools with varied names like BI (Business
Intelligence), ANN (Artificial Neural Network), and Data Mining etc. So,
business people need not
worry about the volume and complexity of computation required, rather they can focus on their core activities and use these techniques to achieve unprecedented business edge.
If we identify one single industry where need of such techniques is highest, it has to be Insurance industry. The reason being that Insurance has scope of coverage in every walk of life and to do it properly in any field, the knowledge level required is as high as that of that field. Assessing the risk involved, covering them and providing value addition to customers in various industries requires complete understanding of the industry and powerful analytics.
In recent years many Insurance service organizations have implemented business intelligence solutions with various names, but mostly these solutions are being used for generating information reports similar to those, which were generated before such implementations. Insurance industry is passing through a very dynamic phase and is looking towards Information Technology for solutions beyond workflow automation, MIS and data processing systems etc.
Business Intelligence, ANN and Data Mining are relatively new terms for business organizations. Operations research, Optimization models, Statistical Techniques etc. are much older and conventional terms. But concepts and theories involved in both these two sets of techniques are very much similar. If we look at the organizations preparedness and technologies available we find a significant advancement in data acquisition and availability of data. But to convert these advancements to real business benefits, there is need of empowerment through technology especially on the analytic part.
Insurance Organizations who have implemented Business Intelligence Software often feel difficulty or inability in deriving real benefits of such implementations. Users of Business Intelligence Software are the same business people. They need to maintain their focus on business aspects. If they try to use Business Intelligence by learning and knowing the operational features available in the software, they are likely to fail in using them. Similarly if they try to understand the computational details and algorithms used in various BI techniques before using them, many of the users may find it too difficult to try it. It's important to understand the meaning of various BI techniques and build concept aiming at understanding the application of these techniques. For Insurance business personnel this approach is effective and easy. It's the users who need to decide about what techniques to be used where and then only they can know which feature of the software to be used. Doing it the other way has been found to be the main reason of BI implementation failure or post implementation failure.
worry about the volume and complexity of computation required, rather they can focus on their core activities and use these techniques to achieve unprecedented business edge.
If we identify one single industry where need of such techniques is highest, it has to be Insurance industry. The reason being that Insurance has scope of coverage in every walk of life and to do it properly in any field, the knowledge level required is as high as that of that field. Assessing the risk involved, covering them and providing value addition to customers in various industries requires complete understanding of the industry and powerful analytics.
In recent years many Insurance service organizations have implemented business intelligence solutions with various names, but mostly these solutions are being used for generating information reports similar to those, which were generated before such implementations. Insurance industry is passing through a very dynamic phase and is looking towards Information Technology for solutions beyond workflow automation, MIS and data processing systems etc.
Business Intelligence, ANN and Data Mining are relatively new terms for business organizations. Operations research, Optimization models, Statistical Techniques etc. are much older and conventional terms. But concepts and theories involved in both these two sets of techniques are very much similar. If we look at the organizations preparedness and technologies available we find a significant advancement in data acquisition and availability of data. But to convert these advancements to real business benefits, there is need of empowerment through technology especially on the analytic part.
Insurance Organizations who have implemented Business Intelligence Software often feel difficulty or inability in deriving real benefits of such implementations. Users of Business Intelligence Software are the same business people. They need to maintain their focus on business aspects. If they try to use Business Intelligence by learning and knowing the operational features available in the software, they are likely to fail in using them. Similarly if they try to understand the computational details and algorithms used in various BI techniques before using them, many of the users may find it too difficult to try it. It's important to understand the meaning of various BI techniques and build concept aiming at understanding the application of these techniques. For Insurance business personnel this approach is effective and easy. It's the users who need to decide about what techniques to be used where and then only they can know which feature of the software to be used. Doing it the other way has been found to be the main reason of BI implementation failure or post implementation failure.
The author is an IT professional working in Insurance Industry. His detail profile can be seen as IT
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