معماری رایانش مه برای توصیه گر شخصی محصولات بانکی
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معماری رایانش مه برای توصیه گر شخصی محصولات بانکی

عنوان فارسی مقاله: معماری رایانش مه برای توصیه گر شخصی محصولات بانکی
عنوان انگلیسی مقاله: Fog computing architecture for personalized recommendation of banking products
مجله/کنفرانس: سیستم های خبره با برنامه های کاربردی - Expert Systems With Applications
رشته های تحصیلی مرتبط: کامپیوتر، مدیریت
گرایش های تحصیلی مرتبط: محاسبات ابری، بانکداری، مدیریت مالی، مدیریت فناوری اطلاعات، سیستم های اطلاعاتی پیشرفته، معماری سیستم های کامپیوتری، مهندسی نرم افزار
کلمات کلیدی فارسی: محاسبات مه، معماری، سیستم توصیه گر، بانکداری تجاری، فین تک
کلمات کلیدی انگلیسی: Fog computing، Architecture، Recommendation system، Commercial banking، Fintech
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2019.112900
دانشگاه: Bisite Research Group, University of Salamanca, Salamanca, Spain
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 5/891 در سال 2019
شاخص H_index: 162 در سال 2020
شاخص SJR: 1/190 در سال 2019
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14313
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Review of fog system solutions


3- Customization of products for financial systems


4- Proposed architecture


5- Conclusions and future lines of work


References

نمونه متن انگلیسی مقاله

Abstract


In this article, a novel Fog Computing solution is proposed, developed in the area of fintech. It integrates predictive systems in the process of delivery of personalized customer services for the recommendation of the products of a banking entity. The motivation behind this research is to improve aspects of customer support services, especially, achieve greater security, increased transparency and agility of processes as well as reduce entity management costs. The presented architecture includes fog nodes where data are processed by light intelligent agents allowing for the implementation of contextual recommendation systems together with the configuration of a Case Based Reasoning in the Cloud layer to improve the efficiency of the whole system over the time. The recommendation system is the cornerstone of architecture operating on banking products, such as mortgages, loans, retirement plans, etc., and it is developed by a hybrid method of recommendation: collaborative filtering combined with content-based filtering. The article analyzes the presented architecture while performing a verification and simulation of the data in the context of commercial banking. For this purpose, it shows the use of the proposed system of recommendations that represent the different communication channels as well as the possible devices. The proposed architecture offers the opportunity to improve the customer service in the bank’s physical channels and at the same time generate technological support to improve the resolution capacity of office managers, allowing employees to adopt a more versatile and flexible role. It also allows the evolution of the banking services model in offices while the processes that support it to follow a one-stop shop approach.


Introduction


Some sectors of the finance industry offer web-data based products and services which cannot be obtained from a bank or a similar provider. This results in a new and competitive environment. The products of the new players range from digital payment solutions and information services, savings and deposit taking, to modern services such as online banking, multi-channel advice and securities trading, not to mention financing solutions (Dapp, 2014). The financial industry is conscious of the need to apply technology to improve its activity; this is reflected in the coinage of the term Fintech or Financial technology, used to denominate the use of computer programs and other technology that supports or enables banking and financial services. In general, the current trend in Fintech developments includes online credits, risk analysis and the treatment of large volumes of data. However, we have detected a lack of technological solutions in the field of commercial banking and personal finance management services which would contribute to improved customer services in financial institutions. In view of this new landscape of solutions that require the capture and processing of a massive volume of data, we propose the use of Fog computing developments.

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