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Sage Business Cases - Bestpay: Technology Empowerment and Innovation Reform
Sage Business Cases - Bestpay: Technology Empowerment and Innovation Reform
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Bestpay: Technology Empowerment and Innovation Reform
By:Mingming Shi, Maoxia Zeng & Shan Liu
Publisher: Renmin University
Publication year: 2020 Online pub date: January 04, 2021
Discipline: Technology Management, Creativity & Innovation in Business, Strategic Management & Business Policy (general)
DOI: https://doi.org/10.4135/9781529767865
Contains: Content Partners | Teaching Notes
Length: 9,018 words
Region: Eastern Asia
Country: China
Industry: Activities auxiliary to financial service and insurance activities| Financial and insurance activities
Originally Published In: Shi, M. , Zeng, M. , & Liu, S. ( 2020). Bestpay: Technology Empowerment and Innovation Reform. Beijing, China: Renmin University Business School.
Organization: Bestpay Co. Ltd
Type: Direct caseinfo
Organization Size: Largeinfo
Online ISBN: 9781529767865
Copyright: © 2020 Renmin Business School. All rights reserved.
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Bestpay (also known as Orange Finance) is the Internet financial platform of China Telecom. In 2018, Bestpay was selected into the “Double Hundred Enterprises” of SASAC. Taking the mixed ownership reform as an opportunity, the company coordinated resources and business with strategic investors, vigorously developed financial technology, and empowered business development with technology. The company upgraded to smart finance through emerging technologies and supported the development of financial services through technological innovations such as intelligent risk control innovation, media innovation, big data technology innovation, and independent R&D innovation. The company has actively explored model innovation, such as relying on the advantage of China Telecom to explore the operator-style differentiated business model of “industry + finance + technology”; taking innovative technology as the cornerstone to build the “Four Platforms” to empower the group’s ecosystem; and taking data technology as the driving force to build a “payment + finance” technology ecosystem of cluster symbiosis and linkage development. In addition, under the guidance of the “3 + 3” policy system at the company and staff level, through the implementation of a series of reforms such as equity diversification and mixed ownership reform, improvement of corporate governance structure, market-oriented operating and incentive mechanism, and restraint mechanism, Orange Finance is transforming into a more market-oriented direction in terms of institutional mechanisms.
This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study,
and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an
endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be
forwarded outside the university or used for other commercial purposes.
2024 Sage Publications, Inc. All Rights Reserved
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Resources Exhibit 1. The Independent R&D Capability and Achievements of Orange Finance Independent R&D Achievement Strengths of the Achievement Orange Finance Automatic Operation and Maintenance Platform Global resource virtualization, rapid expansion and contraction in seconds-level, parallel production and release Orange Finance Automated Testing Framework Full coverage of business test, be selected into network connection technology components Orange Finance Infrastructure Monitoring LaaS layer automatic monitoring, be selected into network connection technology components Orange Finance 3.0 Payment System The system design concept and capability are at the first echelon level of the Internet financial platform Exhibit 2. Orange Finance Builds the G3 Ecosystem with the Composite Business of “Payment + Financial Technology” All text on the left side of the image is within rectangular boxes. The hierarchy in the image is listed below: Orange Finance Payment Sector Enterprise Business Online Business Offline Business Orange International Financial Technology Sector Financial Management Consumer Finance Insurance Agency Credit Investigation Direct Bank Bestpay Red Packet Card The boxes on the left side of the image are grouped together pointing to a flow diagram on the right side of the page. The flow diagram has three ovals such that each oval is connected to the other two ovals by a bidirectional arrow and a straight line. Text in the ovals is listed below: Industry [Image: A silhouette of two buildings] Technology [Image: An illustration with four intersecting ovals and a circle in the center] Finance [Image: A silhouette of a few coins stacked vertically] Text in the area common to the three ovals reads “Customer [Image: A human silhouette].” Exhibit 3. The Payment + Financial Technology Ecosystem of “Orange Finance” All text in the flow diagram is within rectangular boxes connected by unidirectional arrows. A box in the bottom of the flow diagram reads as follows: Orange Finance; Payment Platform (Text is within rectangular box) + Financial Technology (Text is within rectangular box); Attract customers; Increase ARPU, reduce customer detection rate; win-win among partners. The pathway in the flow diagram (from the box) is listed below: Credit platform Cooperative partners Internet of things access Cooperative partners Mobile payment; Smart Life; Intimate experience; Users (Arrow connecting the box is labeled “Financial management Consumer Finance Crowdfunding Orange Credit.”) Online and offline consumption scenario ecosystem; Online shopping; Pay water, electricity, and gas charges; Offline shopping; Pay for property management; Smart home Pay Party membership dues (arrows connecting to the box read “Consumption” and “Supply-chain Finance, Digital management; Precision marketing, Attract customers”) Consumer Data (Arrow connecting the box is labeled “Produce Behavioral data ….”) An arrow labeled “Data collection” from the box labeled “Consumer data” and a rightward arrow from a box labeled “User Data of China Telecom” connects back to the box with heading “Orange Finance.” A box with text “Payment function; Business channels; Marketing platform; Merchants” is connected to the box with the heading “Online and offline consumption scenario ecosystem” by an arrow labeled “aggregation.” Exhibit 4. The Organizational Structure of Orange Finance All text in the image is within rectangular boxes connected by straight lines. The organizational structure is listed below: A round strategic investors (21.26%); China Telecom Co., Ltd. (78.74%) Bestpay Co., Ltd. Market Cooperation Personal Finance Business Group Consumer Finance Business Group Financial Management Business Group Insurance Business Group Red Packet Business Group O2O Business Group Wireless Business Group Enterprise Business Group Enterprise Finance Business Group General Management Department Human Resource Department Financial Department Big Data Department Information Technology Department Customer Service Department Operation Accounts Department Risk Management Department Subsidiaries Net Union Clearing Corporation Shanghai Fufcihong Information Service Co., Ltd. Bestpay Jinzhong Technology Service Co., Ltd. Bestpay Credit Co., Ltd. Orange Insurance Agency Co., Ltd. Bestpay Commercial Factoring Co., Ltd. Orange Financial Leasing Co., Ltd. Changning Zhongshan Small-loan Co., Ltd. Exhibit 5. The Structure of Orange Finance’s Corporate Governance System All text in the image is within rectangular boxes connected by straight lines. The organizational structure is listed below: Party Committee Senior Executives (line connecting the box is labeled “Pre-decision”) Board of Directors Senior Executives Investments Committee Remuneration and Appraisal Committee Nomination Committee Risk Management Committee Board of Supervisors Exhibit 6. The User Scale and Penetration Rate Distribution of Internet Finance Business in Provincial Branches—Take the Data of Bestpay as an Example Heading of the graph reads “User scale and penetration rate distribution of Internet finance business in provincial branches—take the data of Bestpay as an example.” The heading is separated from the graph by a horizontal line. The text below the heading reads “Penetration rate: Monthly average active users of Bestpay/ Users of China Telecom.” The number of users is plotted along the x-axis from 0 to 3,000 with a gap of 500 up to 1,500, and then there is a last entry of 3,000. The penetration rate is plotted along the y-axis from 0% to 30% with a gap of 5%. A horizontal dotted line in the middle of the plot area labeled “Median: 12.3%” and a vertical dotted line in the middle of the plot area labeled “Median: 8.618 Million” intersect each other, dividing the plot area into four segments. Several cities, the penetration rate, and number of active users plotted in the four regions are tabulated below: First segment Cities Penetration rate (%) Number of users Tibet 28 250 Shanxi 27 400 Inner Mongolia 24 350 Jiangxi 18 950 Ninxia 17 270 Beijing 14.5 600 Heilongjiang 14 380 Hainan 13.5 250 Guizhou 14.3 700 Second segment Hebei 26 1600 Henan 18 1000 Fujian 17 1250 Zhejiang 16.5 1500 Anhui 13.5 1490 Third segment Jilin 12.5 280 Liaoning 12.3 400 Xinjiang 12 800 Tianjin 7 150 Qinghai 6.7 150 Yunnan 7.3 700 Fourth segment Shandong 10 910 Chongqing 9.8 900 Shanghai 7 910 Hubei 10.5 1300 Shananxi 10 1250 Guangxi 7.2 1200 Hunan 5 1400 Jiangsu 7.4 1690 Guangdong 9.8 2800 Gansu 12.3 861 Text on the bottom left corner of the graph reads “Amount of users, 10,000.”
This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study,
and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an
endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be
forwarded outside the university or used for other commercial purposes.
2024 Sage Publications, Inc. All Rights Reserved
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Shi, M., Zeng, M., & Liu, S., (2020). Bestpay: Technology empowerment and innovation reform. In Sage Business Cases. SAGE Publications, Ltd., https://doi.org/10.4135/9781529767865
Shi, Mingming, Maoxia Zeng, and Shan Liu."Bestpay: Technology Empowerment and Innovation Reform." In Sage Business Cases. London: Sage Publications Ltd., 2024. https://doi.org/10.4135/9781529767865.
Shi, M., Zeng, M. and Liu, S., 2020. Bestpay: Technology Empowerment and Innovation Reform. London: Sage Publications, Inc. Available at:
Shi, Mingming, et al. "Bestpay: Technology Empowerment and Innovation Reform". Sage Business Cases. London: SAGE Publications, Inc., 2024. 7 Mar 2024, doi: https://doi.org/10.4135/9781529767865.
Shi, Mingming, Maoxia ZengShan Liu. Bestpay: Technology Empowerment and Innovation Reform Sage Business Cases. London: 2020. doi:10.4135/9781529767865. Accessed March 7, 2024.
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research-articleDecentralized and expressive data publish-subscribe scheme in cloud based on attribute-based keyword searchQian XuBlockchain Research Institute, BestPay Co., Ltd, China Telecom, Tanggu Road, Shanghai, China,Qing ZhangBlockchain Research Institute, BestPay Co., Ltd, China Telecom, Tanggu Road, Shanghai, China,Bo YuBlockchain Research Institute, BestPay Co., Ltd, China Telecom, Tanggu Road, Shanghai, China,Nandi ShiBlockchain Research Institute, BestPay Co., Ltd, China Telecom, Tanggu Road, Shanghai, China,Changshuai WangBlockchain Research Institute, BestPay Co., Ltd, China Telecom, Tanggu Road, Shanghai, China,Wei HeBlockchain Research Institute, BestPay Co., Ltd, China Telecom, Tanggu Road, Shanghai, ChinaOctober 2021Journal of Systems Architecture: the EUROMICRO Journal, Volume 119, Issue Chttps://doi.org/10.1016/j.sysarc.2021.102274
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How We Process Data Five Times More Efficiently Using a Scale-Out MySQL Alternative | by PingCAP | Medium
We Process Data Five Times More Efficiently Using a Scale-Out MySQL Alternative | by PingCAP | MediumOpen in appSign upSign inWriteSign upSign inHow We Process Data Five Times More Efficiently Using a Scale-Out MySQL AlternativePingCAP·Follow6 min read·Dec 14, 2020--ListenShareIndustry: Mobile PaymentAuthor: Yu Liu (Senior Architect, China Telecom Bestpay)China Telecom Bestpay is a mobile payment and Internet finance subsidiary of China Telecom Corp. Bestpay supports more than 8 million offline merchant stores and 170 e-business platforms in China, with the commitment to provide a “safe, convenient, and trendy” payment solution. In 2019, Bestpay had 50 million monthly active users, 230 million transactions per month, and 1.75 trillion transactions per year.I am a senior architect at Bestpay. My colleagues and I must constantly improve the efficiency of our software to keep pace with our company’s rapid growth and to respond to fierce market competition. One area in which we have made great strides is our database. After careful investigation, we migrated to TiDB, a MySQL-compatible, open-source, distributed SQL database, which boosted the overall performance of Bestpay’s application system by 3–5 times.In this blog post, I will introduce the outcomes we achieved with TiDB, the difficulties we overcame, how we migrated to TiDB step-by-step, and why we put our faith in TiDB.Outcomes“Adopting TiDB has transformed our system. Not only does it help us meet regulatory specifications, it greatly improves the processing capabilities and efficiency of our financial department. It also reduces the complexity of the technical team’s work.” Bestpay Technical Infrastructure TeamCurrently, Bestpay’s application layer and core platform layer both use TiDB to provide services. Here are some major outcomes we achieved after migrating to TiDB:Risk control supervisionBatch processing performance has improved by more than 3 times.The anti-money laundering system’s processing efficiency has increased by 5 times.Payment reconciliationThe performance of the reconciliation platform has increased by 2 times. For example:To reconcile UnionPay cardless payments, the original MySQL solution took 3–5 minutes. Now, TiDB only takes 1–2 minutes.To reconcile WeChat Pay payments, the original MySQL solution took 3 minutes. Now, TiDB only takes about 1 minute.Personal billingThe user experience and activity have both improved. TiDB solves the issues caused by the previous MySQL sharding solution, including the following:Database capacity: Now, a single table in TiDB has nearly 10 billion rows. Previously with MyCAT, a single table could have a maximum of 100 million rows, and a large table had to be split each month.Data storage duration: With TiDB’s horizontal scalability, data can be stored for 3–5 years or even longer. When we used MySQL, data could only be stored for half a year.Query efficiency: Queries per second (QPS) increased by 50%, and the latency decreased by 20%–30%.Now you’ve got a basic idea of where we stand. However, achieving excellence has never been smooth sailing. We hope that learning about our difficulties and how we overcame them will inspire you in your own work.DifficultiesTo follow government regulations, the calculation for suspicious patterns and risk rating must be completed the day after the payment is made. This requirement poses a challenge to the processing time of our system.Previously, a batch processing task would take 7–8 hours, and the overall task would last for 15 hours or more each day. As the data volume surges, our application system has a greater risk of missing the performance target.We must quickly optimize the database to meet the 2003 SQL standard as well as achieve the following performance requirements :In addition to excellent performance, we want our database to scale so it can support future business growth with great functionality, scalability, stability, and availability.Migration strategyAfter in-depth discussions with the TiDB team, we developed the following three-step migration strategy:Evaluate our application scenarios to put forward a concrete database migration method.Pilot the method on a smaller scale.Go further to the core platforms.ModelingAfter comprehensive tests and evaluation, we decided that new projects using relational databases will choose from three solutions — an internal relational database service (RDS), Sharding-Sphere (S-S), and TiDB. We built an evaluation model that defines guidelines for when to use each solution:The model is based on several dimensions — capacity threshold, performance threshold (QPS), the number of large tables, sharding rules, HTAP capabilities, and topology.TiDB is preferred when:The amount of data is greater than 3 TB.QPS is expected to exceed 20,000.There are more than 20 large tables.Sharding rules are difficult to define.Both transactional and analytical queries are processed.PilotingThe Bestpay application client includes a personal billing system. It allows users to manage, query, classify, and display statistics on their transactions.According to the evaluation model, the personal billing system falls into the TiDB category, so we used it as the pilot project.We then formulated the application database migration principles:Simultaneously write the application data into the old database and the new database running in parallel, and gradually switch traffic to the new one.Data must not be lost or wrong.After verification, sensitive services need to be quickly switched to the new database.During the migration, traffic can be switched to the old database at any time.Some sharded and partitioned tables need to be merged.Following the principles, we used TiDB’s data migration tools to perform the migration task in a short time.EmpoweringAfter the pilot’s success, we continued our quest and chose TiDB to empower two of our core systems — the reconciliation platform system and anti-money laundering system.The reconciliation platform system involves multiple large tables, each of which exceeds 1 billion rows. The total data volume exceeds 8 TB. The application logic is relatively complex, and the data concurrency is moderate.After the migration, the core payment system receives the user transactions and sends them as files to a file analysis program. The program saves the analysis results to the TiDB database. The reconciliation platform reads data from TiDB to complete the reconciliation process and to provide querying services to the web interface.For the anti-money laundering system, the amount and types of monitoring data have increased rapidly. After the migration, the data is replicated from the original Oracle database to TiDB through the Oracle GoldenGate (OGG) for MySQL client; other data on the big data platform are directly replicated from Hive to TiDB using the big data publishing feature.Why we chose TiDB“The TiDB team understands our core business, and we’re impressed with their ability to design and improve a distributed database architecture.” Bestpay Technical Infrastructure TeamFor payment service providers, risk control is top priority. On top of this, we must adapt to business development and evolving application scenarios, and plan for the future. This mindset drives us to adopt a scalable, flexible architectural design and pursue higher data processing efficiency.After the many difficulties we conquered together with the TiDB team, there is no doubt that they are worthy of our trust!What’s nextBestpay will migrate more large-scale and fast-growing core systems to TiDB. These core system databases have hundreds of millions of rows, and a single database stores more than 10 TB of data. The core business must have minimal or zero downtime, which places higher demands on the database.But we believe in TiDB’s capabilities, and we have the confidence and motivation to do this well.Originally published at www.pingcap.com on Nov 19, 2020Financial ServicesDatabaseMySQLDistributed SystemsCase Study----FollowWritten by PingCAP941 FollowersPingCAP is the team behind TiDB, an open-source MySQL compatible NewSQL database. Official website: https://pingcap.com/ GitHub: https://github.com/pingcapFollowHelpStatusAboutCareersBlogPrivacyTermsText to speechTeams关于一卡通“翼支付”充值返利优惠活动的通知-信息网络技术中心-西安电子科技大学
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关于一卡通“翼支付”充值返利优惠活动的通知
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2015年9月15日至10月14日一卡通用户可以参与"翼支付优惠返利"活动,通过翼支付官网或APP充值一卡通可享8折优惠,单个翼支付用户只可享受一次返利,最高返利18元(即充值90元返利18元)。电信、移动、联通用户均可参与,优惠有限,抢完为止!
翼支付注册、登录方式:可以通过手机扫描下方二维码(不建议用微信扫描,其他二维码软件均可)、手机浏览器搜索、安卓市场等第三方平台、登录http://www.bestpay.com.cn等多种渠道下载翼支付手机客户端,并进行安装、注册、登录。
注册、登录过程会收到3条短信,其中1条短信内容涉及支付密码,请进入翼支付后在【帐户】界面进行修改。
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(一)活动时间:2015年11月01日~2015年12月31日。
(二)活动对象:翼支付用户,电信、移动、联通的本地用户均可参与,优惠有限,抢完为止!
(三)活动内容:活动期间每月首次通过翼支付客户端或官网平台,向饭卡充值可享受8.5折立返优惠,单笔最高返利15元,每月可参加一次,活动期间每个翼支付账户返利上限30元。
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Mobile Payment Innovations in China: China UnionPay’s Practice and Experience | SpringerLink
Mobile Payment Innovations in China: China UnionPay’s Practice and Experience | SpringerLink
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Business Innovation with New ICT in the Asia-Pacific: Case Studies pp 257–279Cite as
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Business Innovation with New ICT in the Asia-Pacific: Case Studies
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Mobile Payment Innovations in China: China UnionPay’s Practice and Experience
Quan Sun6,7, Yongkai Zhou6 & Tao Tang6
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First Online: 10 September 2020
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AbstractThe popularization of mobile internet has given rise to the demand for flexible and convenient payment methods. For China, it is necessary to keep pace with or even lead the trend of innovation and development in the age of mobile payment. From the perspective of systems engineering, this chapter introduces the research and practice of China UnionPay’s mobile payment project. The general requirements and core engineering problems of the mobile payment project are summarized based on the analysis of the characteristics and engineering difficulties. Integrated innovation of technologies is introduced to resolve the contradiction between ease-of-use and security. Rapid iterative development process is adopted to improve the product release efficiency as well as user experience. The launch of the mobile payment project also opens the window to coordinate the whole payment industry for upgrading and quality-improving.
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Download references Author informationAuthors and AffiliationsChina UnionPay, Shanghai, ChinaQuan Sun, Yongkai Zhou & Tao TangFudan University, Shanghai, ChinaQuan SunAuthorsQuan SunView author publicationsYou can also search for this author in
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Quan Sun . Editor informationEditors and AffiliationsJapan Advanced Institute of Science and Technology, Nomi, JapanMichitaka Kosaka School of Computer Science, Fudan University, Shanghai, ChinaMichitaka Kosaka School of Computer Science, Fudan University, Shanghai, ChinaJie Wu Advanced Manufacturing and Mechanical Engineering, University of South Australia, Adelaide, SA, AustraliaKe Xing School of Computer Science, Fudan University, Shanghai, ChinaShiyong Zhang Rights and permissionsReprints and permissions Copyright information© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. About this chapterCite this chapterSun, Q., Zhou, Y., Tang, T. (2021). Mobile Payment Innovations in China: China UnionPay’s Practice and Experience.
In: Kosaka, M., Wu, J., Xing, K., Zhang, S. (eds) Business Innovation with New ICT in the Asia-Pacific: Case Studies. Springer, Singapore. https://doi.org/10.1007/978-981-15-7658-4_12Download citation.RIS.ENW.BIBDOI: https://doi.org/10.1007/978-981-15-7658-4_12Published: 10 September 2020
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FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels | SpringerLink
FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels | SpringerLink
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International Conference on Information and Communications SecurityICICS 2019: Information and Communications Security
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Information and Communications Security
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FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels
Ruoyu Deng12, Na Ruan12, Guangsheng Zhang13 & …Xiaohu Zhang13 Show authors
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First Online: 18 February 2020
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Part of the Lecture Notes in Computer Science book series (LNSC,volume 11999)
AbstractAutomated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc., which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. In our work, we propose a semi-supervised learning detection model, FraudJudger, to analyze user behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from raw data with the help of Adversarial Autoencoder (AAE). Compared with other state-of-the-art fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. Besides, we deploy FraudJudger on a real-world financial platform, and the experiment results show that our model can well generalize to other fraud detection contexts.KeywordsFraud detectionAdversarial autoencoderSemi-supervised learning
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Download references AcknowledgmentsOur work is supported by National Nature Science Foundation of China (NSFC) No. 61702330; China Telecom Bestpay Co., Ltd. Author informationAuthors and AffiliationsMoE Key Lab of Artificial Intelligence, Department of CSE, Shanghai Jiao Tong University, Shanghai, ChinaRuoyu Deng & Na RuanChina Telecom Bestpay Co., Ltd., Beijing, ChinaGuangsheng Zhang & Xiaohu ZhangAuthorsRuoyu DengView author publicationsYou can also search for this author in
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Na Ruan . Editor informationEditors and AffiliationsSingapore University of Technology and Design, Singapore, SingaporeJianying Zhou The Hong Kong Polytechnic University, Kowloon, Hong KongXiapu Luo Peking University, Beijing, ChinaQingni Shen Institute of Information Engineering, Beijing, ChinaZhen Xu Rights and permissionsReprints and permissions Copyright information© 2020 Springer Nature Switzerland AG About this paperCite this paperDeng, R., Ruan, N., Zhang, G., Zhang, X. (2020). FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels.
In: Zhou, J., Luo, X., Shen, Q., Xu, Z. (eds) Information and Communications Security. ICICS 2019. Lecture Notes in Computer Science(), vol 11999. Springer, Cham. https://doi.org/10.1007/978-3-030-41579-2_33Download citation.RIS.ENW.BIBDOI: https://doi.org/10.1007/978-3-030-41579-2_33Published: 18 February 2020
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FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels | Information and Communications Security
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FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels Authors: Ruoyu Deng MoE Key Lab of Artificial Intelligence, Department of CSE, Shanghai Jiao Tong University, Shanghai, China MoE Key Lab of Artificial Intelligence, Department of CSE, Shanghai Jiao Tong University, Shanghai, ChinaView Profile , Na Ruan MoE Key Lab of Artificial Intelligence, Department of CSE, Shanghai Jiao Tong University, Shanghai, China MoE Key Lab of Artificial Intelligence, Department of CSE, Shanghai Jiao Tong University, Shanghai, ChinaView Profile , Guangsheng Zhang China Telecom Bestpay Co., Ltd., Beijing, China China Telecom Bestpay Co., Ltd., Beijing, ChinaView Profile , Xiaohu Zhang China Telecom Bestpay Co., Ltd., Beijing, China China Telecom Bestpay Co., Ltd., Beijing, ChinaView Profile Authors Info & Claims Information and Communications Security: 21st International Conference, ICICS 2019, Beijing, China, December 15–17, 2019, Revised Selected PapersDec 2019Pages 569–583https://doi.org/10.1007/978-3-030-41579-2_33Published:15 December 2019Publication History
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AbstractAutomated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc., which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. In our work, we propose a semi-supervised learning detection model, FraudJudger, to analyze user behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from raw data with the help of Adversarial Autoencoder (AAE). Compared with other state-of-the-art fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. Besides, we deploy FraudJudger on a real-world financial platform, and the experiment results show that our model can well generalize to other fraud detection contexts.References1.Aerospike: Enabling digital payments transformation (2019). https://www.aerospike.com/lp/enabling-digital-payments-transformation-ebookGoogle Scholar2.Ahmed MMahmood AIslam MRA survey of anomaly detection techniques in financial domainFuture Gener. Comput. Syst.20155527828810.1016/j.future.2015.01.001Google ScholarDigital Library3.Bahnsen ACAouada DStojanovic AOttersten BFeature engineering strategies for credit card fraud detectionExpert Syst. Appl.20165113414210.1016/j.eswa.2015.12.030Google ScholarDigital Library4.Beutel, A., Xu, W., Guruswami, V., Palow, C., Faloutsos, C.: CopyCatch: stopping group attacks by spotting lockstep behavior in social networks. 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Sci.20166393090311010.1287/mnsc.2016.2489Google ScholarDigital Library19.Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM) (2017)Google Scholar20.Vincent PLarochelle HLajoie IBengio YManzagol PAStacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterionJ. Mach. Learn. Res.201011Dec3371340827561881242.68256Google ScholarDigital Library21.Viswanath, B., et al.: Towards detecting anomalous user behavior in online social networks. In: Proceedings of the 23rd USENIX Security Symposium (USENIX Security), pp. 223–238 (2014)Google Scholar22.West JBhattacharya MIntelligent financial fraud detection: a comprehensive reviewComput. Secur.201657476610.1016/j.cose.2015.09.005Google ScholarDigital Library23.Xiaojin, Z., Zoubin, G.: Learning from labeled and unlabeled data with label propagation. Technical report, Technical Report CMU-CALD-02-107, Carnegie Mellon University (2002)Google Scholar24.Yao, Y., Viswanath, B., Cryan, J., Zheng, H., Zhao, B.Y.: Automated crowdturfing attacks and defenses in online review systems. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS 2017), pp. 1143–1158. ACM (2017)Google Scholar25.Zareapoor MShamsolmoali Pet al.Application of credit card fraud detection: based on bagging ensemble classifierProcedia Comput. Sci.201548201567968510.1016/j.procs.2015.04.201Google Scholar26.Zhang, Y., Liu, G., Zheng, L., Yan, C., Jiang, C.: A novel method of processing class imbalance and its application in transaction fraud detection. In: Proceedings of the IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), pp. 152–159. IEEE (2018)Google Scholar27.Zheng, P., Yuan, S., Wu, X., Li, J., Lu, A.: One-class adversarial nets for fraud detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1286–1293 (2019)Google Scholar28.Zheng YJZhou XHSheng WGXue YChen SYGenerative adversarial network based telecom fraud detection at the receiving bankNeural Netw.2018102788610.1016/j.neunet.2018.02.015Google ScholarDigital Library
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Information and Communications Security: 21st International Conference, ICICS 2019, Beijing, China, December 15–17, 2019, Revised Selected PapersDec 2019834 pagesISBN:978-3-030-41578-5DOI:10.1007/978-3-030-41579-2Editors: Jianying ZhouSingapore University of Technology and Design, Singapore, Singapore,Xiapu LuoThe Hong Kong Polytechnic University, Kowloon, Hong Kong,Qingni ShenPeking University, Beijing, China,Zhen XuInstitute of Information Engineering, Beijing, China
© Springer Nature Switzerland AG 2020SponsorsIn-CooperationPublisherSpringer-VerlagBerlin, Heidelberg
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Published: 15 December 2019
Author TagsFraud detectionAdversarial autoencoderSemi-supervised learningQualifiersArticleConference
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