14-09-2013, 02:29 PM
Data Mining, Banking Sector, Risk Management
Banking Sector.docx (Size: 74.64 KB / Downloads: 24)
Abstract
In the era of globalization and cut throat competition theorganizations today are striving to gain a competitive edge overeach other. Apart from execution of business processes, the creation of knowledge base and its utilization for the benefit of the organization is becoming a strategy tool to compete. The organizations and individuals having right access to the right information at the right moment of time will be the one to rulethe system. In spite of having ever growing data bases the problemis that the banks fail to fully capitalize the true benefits which canbe gained from this great wealth of information. The bankingsector has started realizing the need of the techniques like datamining which can help them to compete in the market. This paperhighlights the perspective applications of data mining to enhancethe performance of some of the core business processes in bankingsector.
Introduction
The computerization of financial operations, connectivity through World Wide Web and the support of automated software’s has completely changed the basic concept of business and the way the business operations are being carried out. The banking sector is not an exception to it. It has also witnessed a tremendous change in the way the banking operations are carried out. Since 1990’s the whole concept of banking has been shifted to centralized databases, online transactions and ATM’s all over the world, which has made banking system technically strong and more customer oriented. In the present day environment, the huge amount of electronic data is being maintained by banks around the globe. The huge size of these data bases makes it impossible for the organizations to analyze these data bases and to retrieve useful information as per the need of the decision makers [3,5]. Since 1980’s the banking sector is incorporating the concept of Management Information System, through which banks are generating various kinds of reports, which are then presented and analyzed for the decision making with in the organization. However these reports available in the summarized form can be used by the governing authorities.
II. Data Mining
Data Mining is the process of extracting knowledge hidden from large volumes of raw data. The knowledge must be new, not obvious, and one must be able to use it. Data mining has been defined as “the nontrivial extraction of implicit, previously unknown, and potentially useful information from data .It is “the science of extracting useful information from large databases” [6]. Data mining is one of the tasks in the process of knowledge discovery from the database shows the process of knowledge discovery.
Clustering
Clustering can be said as identification of similar classes of objects.This is the technique of combining the transactions with similar behavior into one group, or the customers with same set of queries or transactions into one group. Classification approach can also be used as effective mean of distinguishing groups. So clustering can be used as preprocessing approach for attribute subset selection and classification [1]. For Example: The customer of a given geographic location and of a particular job profile demand a particular set of services, like in banking sector the customers from the service class always demand for the policy which ensures more security as they are not intending to take risks, like wise the same set of service class people in rural areas have a the preferences for some particular brands which may differ from their counterparts in urban areas. This information will help the organization in cross-selling their products, Instead of mass pitching a certain “hot” product, the bank’s customer service representatives can be equipped with customer profiles enriched by data mining that help them to identify which products and services are most relevant to callers. This technique will help the management in finding the solution of 80/20 principle of marketing, which says: Twenty per cent of your customers will provide you with 80 per cent of your profits, then problem is to identify those 20 % and the techniques of clustering will help in achieving the same.
Forecasting
Regression technique can be adapted for predication. Regression analysis can be used to model the relationship between one or more independent variables and dependent variables. In data mining independent variables are attributes already known and response variables are what we want to predict [8]. Unfortunately, many real-world problems are not simply prediction. For instance, sales volumes, stock prices, and product failure rates are all very difficult to predict because they may depend on complex interactions of multiple predictor variables [1,8]. Therefore, more complex techniques (e.g., logistic regression, decision trees, or neural nets) may be necessary to forecast future values. This technique of data mining will help in discovering patterns from which one can make reasonable predictions.
Classification
Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules [8,9]. If the accuracy is acceptable, the rules can be applied to the new data tuples. For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis.