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Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk: A Predictive Model For Credit Card Scoring
Blekinge Institute of Technology, School of Management.
Blekinge Institute of Technology, School of Management.
Blekinge Institute of Technology, School of Management.
2009 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification outcomes of this study that neural network gives slightly better results than discriminant analysis and logistic regression. It should be noted that it is not possible to draw a general conclusion that neural network holds better predictive ability than logistic regression and discriminant analysis, because this study covers only one dataset. Moreover, it is comprehensible that a “Bad Accepted” generates much higher costs than a “Good Rejected” and neural network acquires less amount of “Bad Accepted” than discriminant analysis and logistic regression. So, neural network achieves less cost of misclassification for the dataset used in this study. Furthermore, in the final section of this study, an optimization algorithm (Genetic Algorithm) is proposed in order to obtain better classification accuracy through the configurations of the neural network architecture. On the contrary, it is vital to note that the success of any predictive model largely depends on the predictor variables that are selected to use as the model inputs. But it is important to consider some points regarding predictor variables selection, for example, some specific variables are prohibited in some countries, variables all together should provide the highest predictive strength and variables may be judged through statistical analysis etc. This study also covers those concepts about input variables selection standards.

Place, publisher, year, edition, pages
2009. , 32 p.
Keyword [en]
Credit Scoring, Variable Selection, Data Collection and Preparation, Discriminant Analysis, Logistic Regression, Neural Networks, Generic Algorithm, Managerial Implication
National Category
Computer Science Business Administration Probability Theory and Statistics
URN: urn:nbn:se:bth-2099Local ID: diva2:829365
Physics, Chemistry, Mathematics
Available from: 2015-04-22 Created: 2009-06-13 Last updated: 2015-06-30Bibliographically approved

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