A probable effect of the development of internet technology on insurance & banking sectors, decision processes and service structures

The history of the development of the Internet of Things (IoT) covers the last twenty years. Despite the short of time, the concept and implementation of the Internet of Things have widely spread all over the world. The impetus of the dissemination of the concept has exponential speed. In the near future, billions of smart sensors and devices will interact with one another without human intervention. The early impact of the Internet of Things has been observed and discussed in the areas of technology, transportation, production, and marketing. The prospective effect of the Internet of Things on the finance sector has been discussed recently. In this study, the development of the concept of the Internet of Things and it is effect on the finance sector and specifically the insurance and banking sectors and future expectations have been evaluated.

Impact of macroeconomic factors on inflation: An assessment on indian economy by using vector auto-regressive modeling

Inflation is the sustained rise in the prices of commodities. Central Banks have the critical responsibility of ensuring price stability; however, every attempt should be made to ensure that price stability should not hit economic growth. Thus, it becomes imperative for Central Banks to determine the key fiscal and monetary factors that have the greatest impact on domestic price levels. Based on these factors, it can tackle the problem of inflation effectively and efficiently. Further, there are several global price indices and factors that need to be factored in while tackling the problem of inflation. The objective of the current study is to empirically determine the macroeconomic factors that play a significant role in influencing inflation in India. The study considers international food and oil price indices amongst other macroeconomic variables such as the fiscal deficit, index of industrial production, exchange rate, MIBOR, and money supply in order to explain inflation. Monthly data for each of the above variables were collected for the research period 2000-10. The study is based on Vector Auto-Regressive modeling. The Augmented Dickey-Fuller Unit Root Test was performed to test for stationarity of all of the time-series data. The results of the Granger causality tests indicate that fiscal indicators such as fiscal deficit and international factors such as international food and oil price indices play a significant role in influencing inflation. The research outcomes conform to the results of several earlier studies.

Balanced scorecard-based performance assessment of Turkish banking sector with the Analytic Network Process (ANP)

In the last decade, Performance assessment of banking sectors in advanced economies became a prominent issue investment decision. This paper aims to evaluate the balanced-scorecard-based performance of the Turkish banking sector using the Analytic Network Process Approach. Within this scope, all 33 deposit banks were intended to analyze out of 34 banks. Within this scope, we made an analysis in order to determine which perspectives of the balanced scorecard approach are appropriate for each type of bank (state banks, private banks, foreign banks). In this study, we used Analytic Network Process (ANP) approaches so as to achieve this objective. With a balanced-scorecard performance assessment of the banking sector using the ANP approach, all the factor priorities have been extracted and normalized to one for each cluster and final priorities have been obtained. The final priorities and rankings of each perspective of the balanced scorecard and the type of bank ownership have been assessed in the model. According to the results of the analysis, Findings demonstrate that (i) financial factor of balanced scorecard approach has the first rank with 65.7 percent; (ii) Customer perceptive is in the second rank with 22.1 percent. (iii)Third and fourth ranks have close results, (iv) learning and growth stay in the third rank with 6.3 percent (v) internal factor has the weakest importance with 5.9%, (vi) state banks into bank ownership have the highest rank with 53.9 percent, (vii) Private owned banks are the second in the relative performance of the bank groups with 36.1%, (viii) Balanced scorecard based performance of foreign banks are replaced in the last order with approximately 10%.

Method selection for demand forecasting: Application in a private hospital

On the basis of a productive and peaceful society, the physical and mental health of individuals constituting society lies. The threat for health resulting from the deterioration of environmental conditions, genetic inheritance, etc. leads individuals to receive health care services and thus direct them to hospitals. Hospitals in the service sector are enterprises where there is very intense interaction with customers which is difficult to manage. Competition among the institutions involved in this field, to transfer the best service to our customers with cost-efficient confronts us as a necessity. One of the most important points in providing suitable is planning during making a purchase. Particularly in institutions such as hospitals where the purchase of medical supplies is a major cost item, a good demand forecast should be made for the control of inventory costs. In this study, with data taken from a hospital in Istanbul, a demand forecast application was conducted with the actual demand data for the last five years of basic medical materials used. Different forecasting methods were applied to the available data, and it was intended to determine the most appropriate forecasting method.

Visualizing big data via a mixture of PARAMAP and Isomap

Dimension reduction strives to represent higher dimensional data by a lower-dimensional structure. A famous approach by Carroll called Parametric Mapping or PARAMAP (Shepard & Carroll, 1966) works by iterative minimization of a loss function measuring the smoothness or continuity of the mapping from the lower dimensional representation to the original data. The algorithm was revitalized with essential modifications (Akkucuk & Carroll, 2006). Even though the algorithm was modified, it still needed to make a large number of randomly generated starts. In this paper we discuss the use of a variant of the Isomap method (Tenenbaum et al., 2000) to obtain a starting framework to replace the random starts. The core set of landmark points are selected by a special procedure akin to selection of seeds for the k-means algorithm. These core set of landmark points are used to create a rational start for running the PARAMAP algorithm only once but effectively reach a global minimum. Since Isomap is faster and less inclined to local optimum problems than PARAMAP, and the iterative process involved in adding new points to the configuration will be less time consuming (since only one starting configuration is used), we believe the resulting method should be better suited to deal with large data sets, and more prone to obtain an acceptable solution in realistic time.