  A potential objective of every financial organization is to retain existing
customers and attain new prospective customers for long-term. The economic
behaviour of customer and the nature of the organization are controlled by a
prescribed form called Know Your Customer (KYC) in manual banking. Depositor
customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger
etc) are with high risk; whereas in some sectors (Transport Operators,
Auto-delear, religious) are with medium risk; and in remaining sectors (Retail,
Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk for
counterparty can be broadly categorized under quantitative and qualitative
factors. Although there are many existing systems on customer retention as well
as customer attrition systems in bank, these rigorous methods suffers clear and
defined approach to disburse loan in business sector. In the paper, we have
used records of business customers of a retail commercial bank in the city
including rural and urban area of (Tangail city) Bangladesh to analyse the
major transactional determinants of customers and predicting of a model for
prospective sectors in retail bank. To achieve this, data mining approach is
adopted for analysing the challenging issues, where pruned decision tree
classification technique has been used to develop the model and finally tested
its performance with Weka result. Moreover, this paper attempts to build up a
model to predict prospective business sectors in retail banking.
