International Journal of Computer Engineering and Applications, Volume XII, Issue I, Jan. 18, ISSN

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1 A.Komathi, J.Kumutha, Head & Assistant professor, Department of CS&IT, Research scholar, Department of CS&IT, Nadar Saraswathi College of arts and science, Theni. ABSTRACT Data mining techniques are becoming very popular nowadays because of the wide availability of huge quantity of data and the need for transforming such data into knowledge. In today s globalization, core bank model and cut throat competition making banks to struggling to gain a competitive edge over each other. The opposite interaction with customer is no more exists in the customer information, transaction details like deposits and withdrawals, loans, risk profiles, credit card details, credit limit and collateral details related information. Data mining Decision Tree Induction Algorithm is applied to predict the attributes relevant for integrity. The first kind of this model is described in this research paper which can be used by the organization in making the right decision to approve or reject the loan request of the customers. Keywords Data Mining, Risk Management, Classification, Credit Scoring. [1] INTRODUCTION Data mining is the process of discovering or extracting new patterns from large data sets involving methods from statistics and artificial intelligence. Classification and prediction are the techniques used to make out important data classes and predict probable trend.the Decision Tree is an important classification method in data mining classification. It is commonly used in marketing, surveillance, fraud A. Komathi, J.Kumutha, 105

2 detection, scientific discovery. The credit scoring process looks at specific criteria such as income, credit history and many others. All this is done with the intent to reduce the overall default rate thereby decreasing the overall risk of financial institutions such as banks and micro lending institutions. Several credit scoring methodologies have been proposed and implemented and are varied from statistical based methods to Artificial Intelligence based techniques. This paper provides focus on the various algorithms of Decision tree their characteristic, challenges, advantage and disadvantage. [2] DECISION TREES A classification tree is a non-parametric method to analyse dependent and/or categorical variables as a function of continuous explanatory variables (Breimanetal. 1984; Armiger etal, 1997). In a classification tree, a dichotomous tree is built by splitting the records at each node based on a function of a single input. The system considers all possible splits to find the best one, and the winning sub-tree is selected based on its overall error rate or lowest cost of misclassification. Decision trees are particularly useful for classification tasks. Like Radial Basis Neural Networks, decision trees learn from data. Using search heuristics, decision trees are able to find explicit and understandable rules-like relationships among independent and dependent variables. The purpose of the logistic regression model is to obtain a regression equation that could predict in which of two or more groups an object could be placed (i.e. whether a credit should be classified as approved or rejected). Decision trees classify instances by sorting them down the tree from the root to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance and each branch descending from that node corresponds to one of the possible values for this attribute Advantages of Decision Tree algorithms are 1) They generalize in a better way for unobserved instances, once examined the attribute value pair in the training data. 2) They are efficient in computation as it is proportional to the number of training instances observed. 3) The tree interpretation gives a good understanding of how to classify instances based on attributes arranged on the basis of information they provide and makes the classification process selfevident. [3] CREDIT SCORE TECHNIQUE 1.1 Secured Loans and Unsecured Loans In the secured loans, the borrower has to pledge some assets (such as property) as collateral. Most common secured loan is Mortgage loan in which people mortgage their property or asset to get loans. Other example is Gold Loan, Car Loan, Housing loan etc. 1.2 Credit Risk The credit function is the heart of banking. Interest income is the main source of income for any bank. Risk is inherent part of bank s business. Granting any loan to customer always involves some risk. A credit risk is the risk of default on a debt that may arise from a borrower failing to make required A. Komathi, J.Kumutha, 106

3 payments. In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs. In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs. 1.3 Decision Process for Credit Evaluation Credit managers rely heavily upon external data sources to guide them in the credit decision process. To approve or reject a credit request is a delicate task. A credit manager must evaluate the risk associated with extending credit and declining an applicant based on numerous factors [2]. The need for sufficient and reliable information is the foundation of a successful credit decision. A credit manager may call on references, run background checks, pull a credit report, verify bank accounts or ask questions of the applicant to validate the information on the credit application. Credit managers are challenged with the task of obtaining readily available information to support their decision while sending a timely response to the applicant. 1.4 Goals The goal of basic credit score computations using Simple code Architectural Suggestions Clearly in the commercial statistical computing world SAS is the industry leading Product to date. This is partly due to the vast amount of legacy code already in existence in corporations and also because of its memory management and data manipulation capabilities. R in contrast to SAS offers open source support, along with cutting edge algorithms, and facilities. To successfully use R in a large scale industrial environment it is important to run it on large scale computers where memory is plentiful as R, unlike SAS, loads all data into memory. Windows has a 2 gigbayte memory limit which can be problematic for super large data sets. Although SAS is used in many companies as a one stop shop, most statistical departments would benefit in the long run by separating all data manipulation to the database layer (using SQL) which leaves only statistical computing to be performed. Once these 2 functions are decoupled it becomes clear R offers a lot in terms of robust statistical software Practical Suggestions Building high performing models requires skill, ability to conceptualize and understand data relationships, some theory. It is helpful to be versed in the appropriate literature,inspiration relationships that should exist in the data, and test them out. This is an ad hoc process I have used and found to be effective. For formal methods like Geschka s brain writing and Wick s morphological box see Gibson s guide to Systems analysis (Gibson etal, 2004). For the advantages of R and introductory tutorials see Significance and Objective of Study Credit scoring is a very important task for lenders to evaluate the credit applications they receive from customers as well as for insurance companies, which use scoring systems today to evaluate new policy holders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of credit applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labor, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced credit officers, thus helping to control bad debt losses. A. Komathi, J.Kumutha, 107

4 1.4.4 Factors impacting Credit score The exact formula for calculating credit scores is kept a secret by credit bureaus. Each major credit bureau in the US (Experian, Equifax, and TransUnion) uses a proprietary tool developed by the Fair Isaac Corp to calculate a FICO score. The components and their weight age in a credit score, as disclosed by FICO are: Payment history -35%: Late payments on bills, such as a mortgage, credit card or automobile loan, can cause a FICO score to drop. Paying bills on time will improve your FICO score. Credit utilization -30%: The ratio of current revolving debt (such as credit card balances) to the total available revolving credit, or credit limit. You can improve your FICO scores by paying off debt and lowering the credit utilization ratio. Length of credit history -15%: As your credit history ages, it can have a positive impact on the FICO score. Types of credit used -10%: You can benefit by having a history of managing different types of credit, like installment, revolving, consumer finance and mortgage. Recent search of credit -10%: Credit inquiries, which occur when you are seeking new credit, can hurt your score. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government departments employ the same techniques. Credit scoring also has much overlap with data mining, which uses many similar techniques. These techniques combine thousands of factors but are similar or identical. [4] NEED FOR RESEARCH This paper explains the concept of Decision Tree algorithm and Credit score analyzing system. Now we do the research work about the Credit Scoring, in our proposed work the Credit Score will analyze by using data mining Decision Tree algorithm. For this work surely gives the better solution for Loan sanction in banking sectors. This proposed work takes the customer s details and finally give the solution for loan approved. [5] CONCLUSION In this paper, we have offered a two-step loan credibility prediction system that helps the organizations in making the right decision to approve or reject the loan request of the customers. This will certainly help the banking industry to open up efficient delivery channels. Decision Tree Induction Algorithm is used for the prediction. Merging of other techniques that outperform the performance of popular data mining models has to be implemented and tested for the domain. Data mining is the process to extract knowledge from existing data. This model is very useful in decision making for approving loan applications for existing and new customers. We propose to extend the two step credit approval model by including security, capacity and cash-flow parameters in the future research areas. A. Komathi, J.Kumutha, 108

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