Gyeongju-si: Advanced Institute of Convergence I T
摘要:
Since the databases that banks use for analysis of cardholders’ repayment behaviours are usually large and complicated, and the extant classification techniques hardly offer 100% correct classification accuracy so as to possibly incur a considerable loss associated with type II errors, the prediction of cardholders’ future payment behaviours has been still referred to as a difficult task in the credit industry. This paper proposes a two-stage cardholder behavioural scoring model, with merits of artificial neural networks (ANNSs) and data envelopment analysis (DEA), which not only enables banks to verify the ANNSs predicted results of each cardholder’s future repayment behaviour as well as to identify creditworthy cardholders who is profitable with low risks, but also provides guidelines to improve contributions of each inefficient cardholder for card issuer profitability.
關聯:
International Journal of Advancements in Computing Technology 3(2), pp.87-94
