A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

The Impact of ICT on Economic Sectors

As the author could not find a reassuring mathematical and statistical method in the literature for studying the effect of information communication technologyon enterprises, the author suggested a new research andanalysis method that he also used to study the Hungarian economic sectors. The question of what factors have an effecton their net income is vital for enterprises. The highest increment of specific Gross Value Added was produced by thefields of ‘Manufacturing’, ‘Electricity, gas and water supply’,‘Transport, storage and communication’ and ‘Financialintermediation’. With the exception of ‘Electricity, gas andwater supply’, the other economic sectors belong to the groupof underdeveloped branches (below 50%).On the other hand, ‘Construction’, ‘Health and social work’and‘Hotels and restaurants’ can be seen as laggards, so theygot into the lower left part of the coordinate system.‘Agriculture, hunting and forestry’ can also be classified as alaggard economic sector, but as the effect of the compoundindicator on the increment of Gross Value Added was lesssignificant, it can be found in the upper left part of thecoordinate system. Drawing a trend line on the points, it can bemade clear that it shows a positive gradient, that is, the higherthe usage of ICT devices, the higher improvement can bedetected in the specific Gross Value Added

Foreign direct investment in services and manufacturing productivity growth: evidence for Chile

During the 1990s, foreign direct investment in producer service sectors in Latin America was massive. Such investment may increase the quality of services, reduce their cost, and offer opportunities for knowledge spillovers to downstream users of the services. This paper examines the effects of foreign direct investment in services on manufacturing productivity growth in Chile between 1992 and 2004. The authors estimate an extended production function where plant output growth depends on input growth and a weighted measure of foreign direct investment in services. The novelty of the approach is that the authors are able to assess the intensity of usage of various types of services at the plant level and use that information in the estimation of the importance of foreign direct investment in those services. The econometric results show a positive and significant effect of foreign direct investment in services on productivity growth of Chilean manufacturing plants which is robust to a multitude of tests. The economic impact of the estimates is that forward linkages from foreign direct investment in services account for almost 5 percent of the observed increase in Chilean manufacturing productivity growth during the sample period. This evidence therefore suggests that reducing the barriers restricting foreign direct investment in services in many developing economies may help accelerate productivity growth in their manufacturing sectors.Banks&Banking Reform,ICT Policy and Strategies,E-Business,Knowledge Economy,Education for the Knowledge Economy

DATA MINING BASED MODEL AGGREGATION

Applying modelling techniques for getting acquainted with customer behaviour, predicting the customers’ next step is neccessary to keep in competition, by decreasing the capital requirement (Basel II - IRB) or making the portfolio more profitable. According to the easily implementable modelling techniques, data mining solutions widespread in practice. Using these models with no conditions can lead into inconsistent future on portfolio change. Consequence of this situation, contradictory predictions and conclusions come into existence. Recognizing and conscious handling of inconsistent predictions is an important task for experts working on different scene of the knowledge based economy and society. By realizing and solving the problem of inconsistency in modelling processes, the competitive advantage can be increased and strategic decisions can be supported by consistent predictions.model aggregation, consistent future, data mining, CRM, Basel II, Research and Development/Tech Change/Emerging Technologies,

Implementation of Business Intelligence on Banking, Retail, and Educational Industry

Information technology is useful to automate business process involving considerable data transaction in the daily basis. Currently, companies have to tackle large data transaction which is difficult to be handled manually. It is very difficult for a person to manually extract useful information from a large data set despite of the fact that the information may be useful in decision-making process. This article studied and explored the implementation of business intelligence in banking, retail, and educational industries. The article begins with the exposition of business intelligence role in the industries; is followed by an illustration of business intelligence in the industries and finalized with the implication of business intelligence implementation

Application Areas of Data Mining in Indian Retail Banking Sector

Banking systems collect huge amounts of data on day to day basis be it customer information transaction details risk profiles credit card details credit limit and collateral details compliance and Anti Money Laundering AML related information trade finance data SWIFT and telex messages Thousands of decisions are taken in a bank daily These decisions include credit decisions default decisions relationship start up investment decisions AML and Illegal financing related One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions But this is a manual process and is error prone and time consuming due to large volume of transactional and historical data Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process This article explores and reviews various data mining techniques that can be applied in banking areas It provides an overview of data mining techniques and procedures It also provides an insight into how these techniques can be used in banking areas to make the decision making process easier and productiv