Credit risk evaluation as a service (creaas) based on ann and machine learning
Yükleniyor...
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Auricle Technologies
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Credit risk evaluation is the major concern of the banks and financial institutions since there is a huge competition between them to find the minimum risk and maximum amount of credits supplied. Comparing with the other services of the banks like credit cards, value added financial services, account management and money transfers, the majority of their capitals has been used for various types of credits. Even there is a competition among them for finding and serving the low risk customers, these institution shares limited information about the risk and risk related information for the common usage. The purpose of this paper is to explain the service oriented architecture and the decision model for those banks which shares the information about their customers and makes potential customer analysis. Credit Risk Evaluation as a Service system, provides a novel service based information retrieval system submitted by the banks and institutions. The system itself has a sustainable, supervised learning with continuous improvement with the new data submitted. As a main concern of conflict of interest between the institutions trade and privacy information secured for internal usage and full encrypted data gathering and as well as storing architecture with encryption. Proposed system architecture and model is designed mainly for the commercial credits for SME’s due to the complexity and variety of other credits.
Açıklama
Anahtar Kelimeler
Machine Learning, Artificial Neural Networks, Credit Risk, İnformation As A Service, CRIaaS, Clustering, Classification
Kaynak
International Journal on Recent and Innovation Trends in Computing and Communication
WoS Q Değeri
Scopus Q Değeri
Cilt
4
Sayı
4
