Decision Science in Action

There are several fraud claims in insurance industry. The manufacturing industry also suffers with similar fraud claims. The manufacturers sells the product in the market with warranty or guarantee of good quality. They also promise to replace or repair the product according to the agreed policy. Based on the extent of risk and product life pricing of the selling product is decided.  Manufacturers receive several warranty/guarantee claims in a year. Many of them are fraud claims. Identifying the authenticity of claims is rigorous and laborious process. But in the era of the machine learning and data analysis the cost and time of above process can be reduced.  I am discussing here a process that can be used to identify the veracity of the claim. This process framework is based on the machine learning algorithms. I believe that this framework has capability to reduce the time and cost required to investigate any claim manually.  The process flow of the claim review as follows:

In an e-retail setting when a consumer orders products, information are passed to brick and mortar warehouse for delivery of the ordered product.  The brick and mortar warehouse may be owned by same e retailers or may owned by a third party, serving the on-line demands as well as the in-store demands. As most of the in-store retailers have also started selling products on line the layout of the warehouse should be redesigned to reduce the operational cost. So that the replenishment time for the store and delivery time for the on line order can be minimized.  In a traditional warehouse design a separate layout for the delivery of the on line orders has not been studied. Most of the studies focus on the layout of the warehouse designed for the in-store retail type of sales. In this research we study the impact of the on line sales on the warehouse design. When an online orders are received at the warehouse through the website of the e retailer delivery process is initiated. The proces

Social Network Analysis is a well-researched area of computer Science and Mathematics. Any structure which can be represented in the form of edges and nodes is a network. Nodes are connected to each other by edges (sometimes called links). When we study any network, our objective is to evaluate the importance of each node and link. Network Analysis can be used in the areas of genetic engineering, social engineering, marketing, fraud detection, crime detection and economic research etc. Network analysis has been widely used in genetic engineering. Recently the application of network analysis has gained momentum in the field of business marketing and analytical sciences.  In this blog, I will briefly discuss the verticals of businesses in which network analysis can be used to gain insights of customers.  I will exclude the discussion on the application of network analysis in any of the biological fields as I do not have concrete knowledge of this area.    1. Telecom Sector:    The

Multicollinearity is defined as the linear relationship between two or more independent variables while performing regression analysis between a dependent variables and set of independent variables. Multicollinearity presents a severe problem during regression modelling. Inclusion of independent variables having linear relationship with each other leads to parameter estimation with higher standard error. This in turn leads to inaccurate parameter estimation. Furthermore, due to inaccurate parameters regression model becomes unstable. The unstable models performs badly on the validation and test samples. When model is unstable, its performance deteriorate very fast compared to stable model over the period, though model is scored on the data of sample of same population. In such a situation an analyst must investigate for the multicollinearity, before finalizing the model. Next question is how to investigate and which variable should be kept if some variables are found to