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dc.description.abstractAs the world is dependent on monetary stuff, credit has become consequential in our way of life. Therefore, credit scoring has become a comprehensively practiced strategy that helps bank and other financial organizations to evaluate creditworthiness of a client who applied for loans. The core purpose of this research is to carry out a comprehensible assessment of automated credit scoring framework for monetary service applications. The study tries to recognize the major deciding factors for developing an automated system for credit scoring purposes. It suggested a set of features which will be better to use in constructing scoring model for our country. We gave priority on employment, applicant’s salary, previous loan history, purpose of loan, requested loan amount in the optimal feature set. It presents a comparative assessment identified with Statistical and Artificial Intelligence (AI) methods that are utilized for automated credit scoring system. Moreover, it helps us to be aware of most accepted and effective methods practiced in credit scoring system. After comparing different methods, we found Neural Networks and Genetic Programming has higher predictive ability. This analysis notified that there is no supreme statistical approach employed to construct credit scoring framework which works on all circumstances. This study revealed that enhancements are necessary (in the current credit scoring framework) to successfully address every single financial environment. Despite the fact that credit scoring is greatly in practice in developed countries, nevertheless in developing countries it is not executed in numerous financial administrations. By using credit scoring system in our country we can facilitate loans specially micro credits to people who are applied for loan. In this paper, few recommendations are provided for microfinance and micro-lender of developing countries. To actualize better credit scoring framework, few conceivable methodologies were suggested as well. Although, it has great prospect of determining reliability, however credit scoring management is due for a noteworthy overhaul.en_US
dc.subjectAutomated Credit Scoring System, Financial Services, Developing Countries, AI, Machine Learningen_US
dc.titleAn Evaluation of Automated Credit Scoring System for Financial Services in Developing Countriesen_US

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