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Modelling retail bank customers’ needs with Bayesian Belief Networks

Author(s): P. Lakatos, B. Egri & P. Aszalós Abstract: Retail bank customer relationships, like many systems observed in real life, cannot be modelled deterministically - they have multiple variables, interdependent through various channels of cause and effect and are subject to exogenous effects. The methods used with such data extract a model by uncovering stochastic relationships between variables. Bayesian Belief Networks accomplish this effectively because they are capable of integrating experts' knowledge, discovering causal relationships in the data and introducing hidden variables to represent exogenous effects. In this paper we present a method of building a customer relationship model using Bayesian Belief Networks. In addition, we emphasise the need to use concepts employed by compa... Pages: 8 Size: 378 kb Paper DOI: 10.2495/DATA030231

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Download the Full Article This article is part of the WIT OpenView scheme and you can download the full text Adobe PDF article for FREE by clicking the 'Openview' icon to the right.