Abstract
Credit scoring is the assessment of the risk associated with lending to an organization or an individual. Genetic Programming is an evolutionary computational technique that enables computers to solve problems without being explicitly programmed. This paper proposes a genetic programming approach for risk assessment. In particular, the study is set in order to predict, on a collection of real loan data, whether a credit request has to be approved or rejected. The task is to use existing data to develop rules for placing new observations into one of a set of discrete groups. The automation of such decision-making processes can lead to savings in time and money by relieving the load of work on an “expert” who would otherwise consider each new case individually. The proposed model provides good performance in terms of accuracy and error rate.
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Rampone, S., Frattolillo, F., Landolfi, F. (2013). Assessing Consumer Credit Applications by a Genetic Programming Approach. In: Proto, A., Squillante, M., Kacprzyk, J. (eds) Advanced Dynamic Modeling of Economic and Social Systems. Studies in Computational Intelligence, vol 448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32903-6_7
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DOI: https://doi.org/10.1007/978-3-642-32903-6_7
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