Introduction to Paper Presentation on Data Ware Housing and Mining in Banking and Finance Sectors:

Banking and financial sectors are maintaining huge electronic data repositories which store valuable bits of information. So it is very difficult for the human analysts to make decisions patterns with the huge data so data ware housing and data mining concepts are used to increase the performance. In this paper we will discuss the areas of implementation of data mining techniques like portfolio management and risk management, trading and customer profiling in order to increase the business performance.

Brief in to data ware housing and data mining concepts:

Various risk management techniques are used in business management for generating patterns and for exposure of the business. To make integrated measurements we will use market and credit risks methods which focus on the financial models which interact with the overall market. By credit risk method lending decisions are made and they identify the risk in lending to the perspective borrower.

The data mining technique gives a perfect scenario analysis of the capabilities concerned and their asset prices and returns and the risk involved in the market. Portfolio method is used by the risk management which generates the risks at various levels and also stimulates market conditions based on the varying interest and the exchange rates.

Advantages of using Data mining techniques:

By using data mining techniques we can identify the various classes of consumers based on the product by which we can generate a better revenue management. The data mining technique helps to solve the problems generated, as it generates patters on the causality and the correlations which cannot be solved immediately but the sequential checking of the pattern we can come to solution.

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