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In this essay we represent the role of data mining in banks and financial industry.
What is the methods and tools used in this area. We show that the main process is to analysis a huge customer's data then choose the best model to predict suitable action from the valuable information to improve the customer relationship with banks. And what the bank do to have a new customers and generate a new process according the behavior of customer and they interest. Because new technology in this area increase competition in a the finance marketplace.
Data mining in area bank was used to help in decision making process. So, it used to analyze a huge number of data like customers information and there financial transaction in banks or markets, credit risk, trading to enhance the performance of some business processes. There are many different usage of data mining in banks because this area every time need to have a new information to predict a short term movements in the price/value of a product. Data mining is using to evaluate which model is the best to give a height performance and accuracy in some process that depends on the knowledge need to be and the type of information.
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From Default Prediction in Bank Loans Through Data Mining [1]: the objective in this paper is to make a comparative between types of classification methods to have maximize true positive rate and minimize false positive rate on two small samples of dataset from two Indian banks. To build predictive model for credit scoring ( to categorize good or bad credit risk). So, the role of data mining is using methods for classification like decision tree, rule based classifier, statistical classification like bagged, boosted and logistic regression, MLP and RBFNN. To help him to make a model which give the high accuracy. As a result of these comparative they found MLP and RBFNN give the height accuracy to build predictive model. And they found training and testing with random sampling (stratified sampling) is better than cluster prototype.
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From Clustering e-Banking Customer using Data Mining and Marketing
Segmentation [2]: in this paper represent a study on one dataset from
Thailand e-banking. That includes commercial e-banking and governmental ebanking. But e-banking technique is not widely used in Thailand but accepted

in other countries.
This study is just for commercial e-banking. Using SOM,
K-means using for clustering and Apraiori algorithm to find a relationship between a features of e-banking, and RFM are using to segment the customer according there personal profile and e-banking usage. These techniques are used from data mining to analysis the historical data from e-banking usage in commercial banks in Thailand to generate a new service can be used in any customer segment that use e-banking by analysis customer characteristics and behaviors with appropriate criteria: access time, transaction access and RFM analysis. The architecture used in this paper is make a preprocessing for data.
Using RFM to segment customer to group. Applying Kohonen Self
Organizing Maps method SOM to have optimum number of clusters that can be apply in K-mean method. Using RMSSTD an RS to give a best group by minimum error. Applying Apriori association rule to show which transactions happens in each group.
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From Potential Value of Data Mining for Customer Relationship
Marketing in the Banking Industry [3]: the application of data mining in banks are practically without limit. So, data mining can be do many things by analysis mass volume of data specially in bank and financial industry to detect hidden pattern and convert row data to valuable information which is help in risk management, portfolio management, trading, customer profiling and customer relationship management. And there is another applications include credit fraud detection, cross selling of all finance product. One of these things it can be study the behavior of customer and selecting the suitable action to improve the banking industry. These steps are called CRM Customer
Relationship Marketing. Which is kind of on-line e-business where face-toface contact is impossible. CRM includes increase cross-selling possibility, better lead management, better customer response and improved customer loyalty. In this paper using Sample, Explore, Modify and Asses (SEMMA) as evaluated in SAS Institute from data mining methods. And to predict a model using logistic regression, neural network and decision tree. And they found a neural network is more efficient in this area.
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A Data Mining Approach for Retailing Bank Customer Attrition Analysis
[4]: this paper using different technique from data mining to analysis retailing bank customer attrition to retain existing customer and reach new prospective customer. Using lift as a measurement to compare between decision tree, boosted Naïve Bayesians network, selective Bayesian Network, neural
Network. One of the most important issue is the huge volume of data. So, they can't be send for all customers to improve the financial industry. So, make a ranking to customer whose take a height probability then contact with him via mail and phone. The author uses different stages from data mining at first define business problem such as loan service, select the initial data, integrate data, data processing like [data cleansing, statistical analysis, sensitivity analysis, feature selection]. Chose modeling via classification models to predict the likely attriters among the current customers.
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[5]:
The most important data mining method in banks is how to predict the model that gives more accuracy .
Neural network is the best method to generate a model.
Data mining are widely used in bank and financial industry.
Study row information of customers is very difficult but via data mining it is very easy according visualization the relation between attribute so, you can show a hidden information that’s benefit to make a decision.
Manuscript received on January 10, 2006; revised on March
15, 2006.
The authors are with the Department of Computer Science,
Faculty of Science, Kasetsart University, Bangkok 10900,
Thailand; E-mail: waminee@hotmail.com, E-mail: anongnart. s@ku.ac.th, E-mail :fscismi@ku.ac.th
Applied Intelligence 22, 47 –60, 2005 c!
2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
XIAOHUA HU
College of Information Science, Drexel University, Philadelphia, PA, USA 19104 xiaohua hu@acm.org; thu@cis.drexel.edu
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