افزایش گنجایش مالی با استفاده از داده تلفن همراه و تحلیل شبکه های اجتماعی
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افزایش گنجایش مالی با استفاده از داده تلفن همراه و تحلیل شبکه های اجتماعی

عنوان فارسی مقاله: ارزش کلان داده برای رتبه بندی اعتباری: افزایش گنجایش مالی با استفاده از داده های تلفن همراه و تجزیه و تحلیل شبکه های اجتماعی
عنوان انگلیسی مقاله: The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، مدیریت سیستم های اطلاعاتی، سامانه های شبکه ای
کلمات کلیدی فارسی: رتبه بندی اعتبار، تجزیه و تحلیل شبکه های اجتماعی، میزان سود، داده های تلفن همراه، پشتیبانی تصمیم
کلمات کلیدی انگلیسی: Credit scoring، Social network analysis، Profit measure، Mobile phone data، Decision support
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2018.10.004
دانشگاه: Department of Decision Sciences and Information Management, KU Leuven, Belgium
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 6/031 در سال 2018
شاخص H_index: 110 در سال 2019
شاخص SJR: 1/216 در سال 2018
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 36
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11306
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Methodology


4- Experimental design


5- Results


6- Discussion


7- Impact of research


8- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.


Introduction


Credit scoring is undoubtedly one of the oldest applications of analytics where lenders and financial institutions perform statistical analysis to assess the creditworthiness of potential borrowers to help them decide whether or not to grant credit [1]. Fair Isaac was founded in 1956 as one of the first analytical companies offering retail credit scoring services in the US. Its well-known FICO score (ranging between 300 and 850) has been used as a key decision instrument by financial institutions, insurers, utilities companies and even employers [2]. The first corporate credit scoring models date back to the late sixties with Edward Altman developing his well-known z-score model for bankruptcy prediction, which is still used to this day in Bloomberg reports as a default risk benchmark [3]. Originally, these models were built using limited data–consisting of only a few hundred observations–and were based on simple classification techniques such as linear programming, discriminant analysis and logistic regression, which is the current industry standard given its high interpretability [2]. The importance of these retail and corporate credit scoring models further increased due to various regulatory compliance guidelines such as the Basel Accords and IFRS 9 which clearly stipulate the inputs and outputs of a credit scoring model together with how these models can be used to calculate provisions and capital buffers.

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