Prepared by : Lamiya El- Saedi 220093158

1. ESSAY The role of data Mining in Banks industry Prepared by:LamiyaEl-Saedi220093158

2. Abstract Table of content: Introduction Related Work Conclusion References

3. In this essay we represent the role of data mining in banks and financial industry. Abstract: 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. • Because new technology in this area increase competition in a the finance marketplace. • And what the bank do to have a new customers and generate a new process according the behavior of customer and they interest.

4. Data mining in area bank was used to help in decision making process. Introduction: 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.

5. From Default Prediction in Bank Loans Through Data Mining [1]: 2006 Related Work: • 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.

6. From Default Prediction in Bank Loans Through Data Mining [1]: • 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.

7. In this paper represent a study on one dataset from Thailand e-banking. That includes commercial e-banking and governmental e-banking. • But e-banking technique is not widely used in Thailand but accepted in other countries. From Clustering e-Banking Customer using Data Mining and Marketing Segmentation [2]: 2006 • 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.

8. From Clustering e-Banking Customer using Data Mining and Marketing Segmentation [2]: • 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.

9. From Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry [3]: 2009 • 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-to-face contact is impossible. • CRM includes increase cross-selling possibility, better lead management, better customer response and improved customer loyalty. • 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-to-face 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.

10. From Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry [3]: • 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.

11. this paper using different technique from data mining to analysis retailing bank customer attrition to retain existing customer and reach new prospective customer. A Data Mining Approach for Retailing Bank Customer Attrition Analysis[4]: 2005 • 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.

12. A Data Mining Approach for Retailing Bank Customer Attrition Analysis[4]: • 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.

13. This paper are notes and advices for bankers, who would like to get a possible applications of data mining, to enhance the performance of their core business process because this area have a wide range for competitive. DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKER [5]: 2008 • and the author discussed a broad of application like risk management, portfolio management, trading customer, profiling and customer care. • Risk Management is the major challenge in banking and insurance world. So, the implementation of risk management systems to identify, measure and control business exposure.

14. DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKER [5]: 2008 • You can use data mining to make different purpose such as • understanding business performance, • making new marketing initiatives, • market segmentation, • risk analysis and • revising company customer policies. • Since data mining can be deal with large amount of data to generate rules and models that are useful in enabling decisions that can be applied in a future.

15. DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKER [5]: 2008 • For classification you can classified into "risky" or "safe" group by using Decision Tree and Rule induction techniques to build a model and predict new loan application. • You can make a prediction to predict expected default amounts for new loan application. • So, the most common data mining method is clustering, classification, association rule and sequential pattern discovery. • And because there is a high competitive in finance industry Neural Network was using for intelligent business marketing.

16. 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. • Clustering also important in financial industry to segmentation and group each type of customers according they behavior and what kinds of application they favorite. So, they can deal with customer in a proper way to enhance bank and financial industry. Conclusion:

17. [1] S. S. Satchidananda, Jay B. Simha, Default Prediction in Bank Loans through Data Mining, CBIT-IIITB Working Paper WP-2006-11 [2] W. Niyagas, A. Srivihok and S. Kitisin, Clustering e-Banking Customer using Data Mining and Marketing Segmentation March 2006, 15. [3] Ogwueleka, F. Nonyelum: Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry: Adv. in Nat. Appl. Sci., 3(1): pp 73-78, 2009 [4] X. HU, A Data Mining Approach for Retailing Bank Customer Attrition Analysis, Applied Intelligence 22, pp 47–60, 2005 [5] R. Dass, Data Mining In Banking And Finance: A Note For Bankers, published paper 2008. References: