本文選題：電商金融 + 大數據征信��； 參考：《江西理工大學》2017年碩士論文

【摘要】：隨著搜索引擎、云計算、人工智能這些新興技術的成熟和普及,人類在日常中產生的數據量出現了前所未有的爆發(fā)式增長,催生了“大數據”時代的到來。在這種背景下,互聯網與傳統(tǒng)金融業(yè)的“碰撞”使得互聯網金融應運而生�；ヂ摼W金融的誕生滿足了中小微企業(yè)和大眾金融消費者的需求,彌補了傳統(tǒng)金融機構的不足,為普惠金融的發(fā)展提供新的思路。其中,以電子商務平臺為核心的電商金融在所有互聯網金融模式中影響最大,引起了整個行業(yè)和社會的高度關注。電商金融行業(yè)本身就是一個基于數據的產業(yè),行業(yè)內擁有著大量的多源異構數據,一方面是自身內部電商平臺的海量歷史交易數據;另一方面是互聯網和社交媒體上的外部數據。因此,如何具備從電商金融大數據中提取和挖掘所蘊含數據價值的能力將決定未來整個電商金融行業(yè)的競爭力。本文針對上述問題,在分析電商金融大數據特征及價值、國內外基于空間關聯規(guī)則的挖掘方法以及大數據挖掘研究現狀的基礎上,采用分布式搜索引擎技術,定制網絡爬蟲從電商金融行業(yè)的多源異構數據中獲取所需要的銀行卡和淘寶店鋪數據,設計相應的Spark并行算法對數據預處理,建立倒排表和二級索引文件,為后面的大數據分析平臺提供數據源。確定數據來源后,運用MECE分析法并結合行業(yè)內多位金融業(yè)務專家評分得到企業(yè)信用風險評價候選指標集及量化方法,分析指標相關性和風險定級。接著,利用大數據機器學習庫中的隨機森林算法對候選指標集特征選擇,設計基于Hash結構的多級空間關聯規(guī)則算法來挖掘企業(yè)風險信息,構建出信用風險評估與智能預警模型。最后,將機器學習、挖掘算法庫、信用風險評估與智能預警模型、大數據存儲與分布式計算能力進行封裝,搭建基于Spark on YARN的電商金融大數據分析平臺,對所研究模型的準確度和平臺實用性進行驗證。以淘寶平臺某旗艦店一年的日常經營數據、銀行卡資金往來數據和管理層群體數據作為數據源,利用電商金融大數據分析平臺對店鋪進行經營行為分析,提供信用風險評估與審批授信和貸后風險預警管理服務,證明構建的信用風險評估與智能預警模型能夠達到預期要求,具有較高的可信度。
[Abstract]:With the maturity and popularization of search engine, cloud computing and artificial intelligence, the amount of data generated by human beings in the daily life has increased dramatically, and the era of "big data" has come into being. Under this background, the collision between Internet and traditional financial industry makes Internet finance emerge as the times require. The birth of Internet finance meets the needs of small and medium-sized enterprises and consumers of popular finance, makes up for the shortcomings of traditional financial institutions, and provides a new way of thinking for the development of inclusive finance. Among them, the electronic commerce finance with the electronic commerce platform as the core has the biggest influence in all the Internet finance models, which has aroused the high attention of the whole industry and the society. E-commerce finance industry itself is a data-based industry, the industry has a large number of multi-source heterogeneous data, on the one hand, the internal e-commerce platform of the massive historical transaction data; On the other hand are external data on the Internet and social media. Therefore, how to extract and mine the data value from the e-commerce finance big data will determine the competitiveness of the entire e-commerce finance industry in the future. In this paper, based on the analysis of the characteristics and value of big data in e-commerce finance, the mining methods based on spatial association rules and the current situation of big data mining, the distributed search engine technology is adopted in this paper. The customized web crawler acquires the bank card and Taobao store data from the multi-source heterogeneous data of the e-commerce finance industry, designs the corresponding Spark parallel algorithm to preprocess the data, and establishes the inverted list and the secondary index file. Provide data sources for later big data analysis platforms. After the data source is determined, the enterprise credit risk evaluation candidate index set and quantitative method are obtained by using MECE analysis method and combining with the score of many financial business experts in the industry, and the correlation and risk grading of the index are analyzed. Then, using the stochastic forest algorithm in big data machine learning library to select the feature of candidate index set, a multi-level spatial association rule algorithm based on Hash structure is designed to mine enterprise risk information, and a credit risk assessment and intelligent early warning model is constructed. Finally, the machine learning, mining algorithm library, credit risk assessment and intelligent early warning model, big data storage and distributed computing ability are encapsulated, and the big data analysis platform of e-commerce finance based on Spark on YARN is built. The accuracy and practicability of the model are verified. Taking the daily management data of a flagship store on Taobao platform, bank card fund data and management group data as the data source, the big data analysis platform of e-commerce finance is used to analyze the business behavior of the store. It is proved that the established credit risk assessment and intelligent early-warning model can meet the expected requirements and have a high credibility by providing the services of credit risk assessment and approval and post-loan risk early warning management.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13

【參考文獻】

相關期刊論文 前10條

1 周成虎;;大數據時代的空間數據價值——《空間數據挖掘理論與應用》評介[J];地理學報;2016年07期

2 王偉;陳偉;祝效國;王洪偉;;眾籌融資成功率與語言風格的說服性——基于Kickstarter的實證研究[J];管理世界;2016年05期

3 朱宇峰;蘭小機;康俊鋒;;旅游地理信息垂直搜索引擎及應用研究[J];測繪科學;2016年05期

4 侯敬文;程功勛;;大數據時代我國金融數據的服務創(chuàng)新[J];財經科學;2015年10期

5 劉國平;;電商金融的運作模式與商業(yè)銀行的應對策略[J];新金融;2015年08期

6 武若楠;;基于以電商平臺為核心的互聯網金融研究[J];時代金融;2015年11期

7 李清泉;李德仁;;大數據GIS[J];武漢大學學報(信息科學版);2014年06期

8 郭遲;劉經南;方媛;羅夢;崔競松;;位置大數據的價值提取與協同挖掘方法[J];軟件學報;2014年04期

9 于艷華;宋美娜;;大數據[J];中興通訊技術;2013年01期

10 路永和;李焰鋒;;改進TF-IDF算法的文本特征項權值計算方法[J];圖書情報工作;2013年03期

相關會議論文 前1條

1 呂琳;朱東華;劉玉琴;;面向數據倉庫的數據預處理研究綜述[A];2007年中國智能自動化會議論文集[C];2007年

相關碩士學位論文 前5條

1 張日金;我國P2P網絡借貸風險控制研究[D];浙江大學;2015年

2 樊嘉麒;基于大數據的數據挖掘引擎[D];北京郵電大學;2015年

3 談浩;互聯網金融和小額貸款研究[D];上海交通大學;2013年

4 楊宸鑄;基于HADOOP的數據挖掘研究[D];重慶大學;2010年

5 方剛;空間關聯規(guī)則挖掘算法的研究與應用[D];電子科技大學;2009年

，

本文編號：1959906