A Data Mining Approach to Predict Prospective Business Sectors for Lending in Retail Banking Using Decision Tree

TL;DR

This paper develops a decision tree-based data mining model to predict prospective business sectors for lending in retail banking, aiming to improve loan disbursement strategies based on customer risk profiles.

Contribution

It introduces a novel application of pruned decision trees to classify and predict high, medium, and low-risk business sectors for retail bank lending.

Findings

The model effectively classifies customer sectors with high accuracy.

Decision tree approach outperforms traditional methods in sector prediction.

The study provides a practical tool for banks to assess lending risks.

Abstract

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.