Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison

Abstract

Banking system collect enormous amounts of data every day. This data can be in the form of customer information, transaction details, risk profiles, credit card details, limits and collateral details, compliance Anti Money Laundering (AML) related information, trade finance data, SWIFT and telex messages. In addition, Thousands of decision are made in Banking system. For example, banks everyday creates credit decisions, relationship start up, investment decisions, AML and Illegal financing related decision. To create this decision, comprehensive review on various reports and drills down tools provided by the banking systems is needed. However, this is a manual process which is error prone and time consuming due to large volume of transactional and historical data available. Hence, automatic knowledge mining is needed to ease the decision making process. This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms. This research provides an overview of the data mining techniques and procedures will be performed. It also provides an insight into how these techniques can be used in deposit subscription in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.

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