Andere Kunden interessierten sich auch für
![Improving Book Lending Service in UTM Library with Apriori Rule-mining]( "Improving Book Lending Service in UTM Library with Apriori Rule-mining")
Improving Book Lending Service in UTM Library with Apriori Rule-mining40,99 €![Web Personalization Models using Computational Intelligence]( "Web Personalization Models using Computational Intelligence")
Web Personalization Models using Computational Intelligence44,99 €![Computational Intelligence Techniques for Direct and Inverse Modelling]( "Computational Intelligence Techniques for Direct and Inverse Modelling")
Computational Intelligence Techniques for Direct and Inverse Modelling48,99 €![Computational Intelligence Techniques on Cardiac Arrhythmia]( "Computational Intelligence Techniques on Cardiac Arrhythmia")
Computational Intelligence Techniques on Cardiac Arrhythmia51,99 €![Computational Intelligence for On-line Collaborative Learning]( "Computational Intelligence for On-line Collaborative Learning")
Computational Intelligence for On-line Collaborative Learning51,99 €![Computational Intelligence in Computer Aided Medical Diagnosis]( "Computational Intelligence in Computer Aided Medical Diagnosis")
Computational Intelligence in Computer Aided Medical Diagnosis51,99 €![Email Filtering Based On Swarm Intelligence via Machine Learning]( "Email Filtering Based On Swarm Intelligence via Machine Learning")
Email Filtering Based On Swarm Intelligence via Machine Learning46,99 €
Arrears management is the process by which loan accounts that have defaulted on their contractual repayments are managed. Although computational intelligence approaches such as artificial neural networks have been applied to the related field of loan approval/credit-scoring, there is little evidence of their application to arrears management. This thesis investigates the feasibility of using artificial neural networks to predict the risk of personal loan accounts falling into arrears. The data used is real live personal loan data sourced from a medium-sized Australian financial institution. The results are very encouraging, particularly those obtained when an ensemble rather than individual networks were used. The results also suggested that there is scope for further substantial research in this area.