Inlämning av Examensarbete / Submission of Thesis

Md. Samsul Islam; Lin Zhou; Fei Li , pp. 32. MAM/Sektionen för Management, 2009.

The work

Författare / Author: Md. Samsul Islam, Lin Zhou, Fei Li
Titel / Title: Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk: A Predictive Model For Credit Card Scoring
Abstrakt Abstract:

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.

Ämnesord / Subject: Företagsekonomi - Business Administration\Information
Datavetenskap - Computer Science\Artificial Intelligence
Mathematics\Probability and Statistics
Nyckelord / Keywords: Credit Scoring, Variable Selection, Data Collection and Preparation, Discriminant Analysis, Logistic Regression, Neural Networks, Generic Algorithm, Managerial Implication

Publication info

Dokument id / Document id:
Program:/ Programme Business Administration
Registreringsdatum / Date of registration: 06/13/2009
Uppsatstyp / Type of thesis: Magisterarbete/Master's Thesis (60 credits)

Context

Handledare / Supervisor: Mr. Anders Hederstierna
Organisation / Organisation: Blekinge Institute of Technology
Institution / School: MAM/Sektionen för Management
S-372 25 Ronneby
+46 455 38 50 00

Files & Access

Bifogad uppsats fil(er) / Files attached: thesis.pdf (1016 kB, öppnas i nytt fönster)