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 …