دانلود مقاله طراحی روند داده کاوی برای حوزه خدمات مالی
چکیده
مقدمه
پیش زمینه و مطالب مرتبط
طرح پژوهش
توسعه FIN-DM
بحث
نتیجه گیری و چشم انداز
منابع
Abstract
Introduction
Background and related work
Research design
FIN-DM development
Discussion
Conclusion and outlook
References
چکیده
اجرای پروژه های داده کاوی در سازمان های پیچیده نیازمند فرآیندهای کاملاً تعریف شده ای است. فرآیندهای استاندارد داده کاوی، مانند CRISP-DM، در دو دهه گذشته مورد استقبال گسترده قرار گرفته است. با این حال، مطالعات متعدد نشان داد که سازمان‌ها اغلب CRISP-DM و فرآیندهای مرتبط را آنطور که هست اعمال نمی‌کنند، بلکه آنها را برای رسیدگی به الزامات خاص صنعت تطبیق می‌دهند. بر این اساس، تعدادی انطباق خاص بخش از فرآیندهای داده کاوی استاندارد پیشنهاد شده است. با این حال، تاکنون چنین سازگاری برای بخش خدمات مالی پیشنهاد نشده است. این مقاله با طراحی و ارزیابی یک فرآیند صنعت مالی برای داده کاوی (FIN-DM) به این شکاف می پردازد. FIN-DM CRISP-DM را برای رسیدگی به الزامات انطباق مقرراتی، حاکمیت و مدیریت ریسک ذاتی در بخش مالی، و برای تعبیه تضمین کیفیت به عنوان بخشی جدایی ناپذیر از چرخه عمر پروژه داده کاوی، تطبیق داده و گسترش می دهد. این چارچوب به طور مکرر با متخصصان داده کاوی و فناوری اطلاعات در یک سازمان خدمات مالی طراحی و تأیید شده است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The implementation of data mining projects in complex organisations requires well-defined processes. Standard data mining processes, such as CRISP-DM, have gained broad adoption over the past two decades. However, numerous studies demonstrated that organisations often do not apply CRISP-DM and related processes as-is, but rather adapt them to address industry-specific requirements. Accordingly, a number of sector-specific adaptations of standard data mining processes have been proposed. So far, however, no such adaptation has been suggested for the financial services sector. This paper addresses the gap by designing and evaluating a Financial Industry Process for Data Mining (FIN-DM). FIN-DM adapts and extends CRISP-DM to address regulatory compliance, governance, and risk management requirements inherent in the financial sector, and to embed quality assurance as an integral part of the data mining project life-cycle. The framework has been iteratively designed and validated with data mining and IT experts in a financial services organisation.
Introduction
Over the past decades, data mining practices have been widely adopted among organisations seeking to maintain and to enhance their competitiveness and business value (Davenport & Harris, 2017). This trend has led a number of large organisations to manage a rich portfolio of data mining projects (Davenport & Harris, 2017). The successful development, implementation, and management of data mining projects in such organisations requires a structured and repeatable approach. Accordingly, academia and industry practitioners have proposed several guidelines and standard processes for conducting data mining projects (Mariscal et al., 2010), most notably CRISP-DM1 – a standard process that captures a wide range of recurrent data mining tasks and deliverables structured around a project life-cycle (Marban, Mariscal et al., 2009).
Conclusion and outlook
This research has presented the design and evaluation of a data mining reference process for the financial services domain – FIN-DM. Guided by DSRM methodology, and combining behavioural science and design science paradigms, we developed and proposed design artefact. FIN-DM was designed to tackle the gaps in the CRISP-DM standard process, which have been identified in previous studies on the use of CRISP-DM in the financial industry. We, first, consolidated evidence from earlier studies on the gaps of CRISP-DM. We proceeded with formulating the requirements and design aspects of the new data mining process, FIN-DM, and developing its prototype. Next, we evaluated the prototype by conducting demo sessions and semi-structured interviews with the experienced data mining and IT practitioners actively engaged in data mining projects in the financial services industry. We also constructed and distributed a qualitative questionnaire among the pre-selected group of data scientists. Finally, we integrated feedback received from the evaluation to improve the final version of FIN-DM.