<![CDATA[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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