STATE BANK OF VIETNAM BANKING ACADEMY. International Conference, BDBF 2017 Hanoi, Vietnam, June 15 th 2017 Proceedings

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1 STATE BANK OF VIETNAM BANKING ACADEMY International Conference, BDBF 2017 Hanoi, Vietnam, June 15 th 2017 Proceedings

2 PREFACE n the context of new data and information sources rapidly expanding in daily business activities, the phenomenon of Big Data and its applications are ever more present, ever more pervasive, and ever more important. Big Data plays an important role in the growth strategy of banking and financial services worldwide where the volume of business data doubles every 14 months, according to estimates. Big Data, as a leading trend in data analytics solutions, is very powerful for decision-making and revealing valuable business insights faster and better than otherwise possible with traditional Business Intelligence. Therefore, Big Data can be seen as an offering opportunity for banks and financial firms in Vietnam to better business processes, understand business trends and improve business performance. Big Data for Banking and Financial industry BDBF 2017, organized by the Banking Academy State Bank of Vietnam in Hanoi on June 15 th, 2017, aims to foster research on Big Data and its prospect in banking and financial industry in Vietnam. The conference brings together practitioners and academics to discuss different perspectives of Big Data including latest technological advances and how banks and financial institutions can benefit from Big Data as well as overcome potential challenges when leveraging Big Data solutions. We are pleased to receive contributions from universities and institutions comprised of Banking Academy of Vietnam, Banking University of Ho Chi Minh City, National Economics University, Institute of Information Technology VAST, University of Commerce, Duy Tan University, Electric Power University, Leibniz University Hannover; from high-tech companies (Salesforce, Dell EMC, VEEAM, Gimasys, IPC Global Asia Pacific, Amazon Web Services) and from Vietnamese commercial banks (Vietcombank, VPComBank). This volume of proceedings provides an opportunity for readers to engage with a selection of papers that were presented during the BDBF 2017 Conference. With 23 articles, 09 presentations and 05 solution demonstrations, the conference should bring to the audiences ii

3 diverse perspectives of Big Data and its applications in banks and financial organizations. The reader will sample here reports of research on topics such as advanced data mining algorithms; conceptual frameworks for applying Big Data in organizations and state-ofthe-art solutions for resolving business challenges with Big Data. We hope you enjoy reading and find this volume valuable to your professional development. If you have any comments about the conference, please do not hesitate to contact us. We would welcome and appreciate your advice. Hanoi, June 2017 On behalf of Editorial Board Dr. Phan Thanh Duc Faculty of Management Information Systems The Banking Academy of Vietnam iii

4 TABLE OF CONTENTS A Study on the Application of Big Data in French Banking Industry and Experience Lesson Drawn to Apply for Vietnam Banking Industry... 1 Han Viet Thuan and Phan Thanh Duc Applying Big Data to Improve Customer Experience in the Commercial Bank... 7 Nguyen Van Thuy Big Data Analysis for Credit Scoring Nguyen Huu Hai, Nguyen Truong Thang and Nguyen Viet Anh Business Intelligent Competency Center in Banking Industry Nguyen Duc Business Process Enhancement with Process Mining in Commercial Banks Mai Tan Tai and Chu Van Huy Semantic Similarity of Entries on Social Media Based on Wikipedia Nguyen Thi Hoi and Dam Gia Manh A New Method for Trace Clustering in Process Mining Bui Thi Hong Nhung, Ngo Thuy Linh and Nguyen Thi Thu Trang A New Secure Sum Protocol Vu Duy Hien and Do Thi Quynh Anh Mathematical Model of PageRank Algorithm Nguyen Thi Thuy Anh, Le Thi Hong Nhung and Ngo Thuy Linh Some Statistical Method for Analyzing Big Data and Applications: A Survey Le Si Dong, Ta Quoc Bao and Ha Binh Minh A Solution for Building Intelligence Estate Information System Based on Big Data Nguyen Thi Thu Ha, Truong Huy Hoang, Nguyen Ngoc Linh and Trinh Tung An Approach to the Implementation of Big Data Project on Customer Relationship Management at Commercial Bank in Vietnam Phan Thanh Duc, Le Ngoc Tu, Nguyen Quynh Anh, Tran Phuong Lan and Chu Thi Huyen Trang iv

5 Appy Mask to Enhance Detection of Financial Fraud for Enterprise Nghiem Thi Lich and Nguyen Thi Toan Applying SVM Networks in Foreign Exchange Market Nguyen Thi Thu Thuy Big Data and the Ability to Promote the Marketing Effectively in Vietnam Enterprises Chu Van Huy and Mai Tan Tai Studying and applying the Elgamal public key cryptosystem to the documents information security at National Economics University Luu Minh Tuan, Han Viet Thuan and Nguyen Trung Tuan Business Intelligence Application for Suppor Decision Le Van Hung and An Phuong Diep Deep Learning for Credit Scoring in the Era of Big Data Le Quy Tai and Giang Thi Thu Huyen Factor That Influence the Adoption of Internet Banking in Vietnam Tran Thi Hue and Nguyen Thanh Thuy Personalized Banking Services using Recommender Systems: a Proposal Cao Thi Nham and Vo Van Luong Stock Price Estimates of Ocean Group Joint Stock Company with ARIMA model Le Van Hung and An Phuong Diep The Application of Big Data in the Commercial B Credit Activities Do Thi Van Trang, Bui Ngoc Phuong Big Data Approach toward Measurement of Customer Lifetime Value and Market Segmentation: The Case of Telecommunication Sector in Vietnam Ha Hien, Nguyen Kim Thanh and Tu Van Binh Social Network Analysis for Vietnamese Commercial Banks: Applications, Principles and Challenges Dinh Trong Hieu v

6 A STUDY ON THE APPLICATION OF BIG DATA IN FRENCH BANKING INDUSTRY Han Viet Thuan 1, Phan Thanh Duc 2 1 thuanhv@neu.edu.vn, 2 ducpt@bav.edu.vn 1 National Economics University, Hanoi, Vietnam 2 Faculty of Management Information Systems, The Banking Academy of Vietnam, Hanoi, Vietnam Abstract. In the context of the economic and social development of our world today, data and information resources are growing at an extremely fast pace. Big Data, an inherent feature of cloud computing, will offer unprecedented opportunities for both traditional and multimedia networks. Exploiting Big Data has become an indispensable trend in the socio-economic development process, which leads to profound changes in the financial and banking sector. Vietnam is in the process of deep integration with the world, including the integration of science and technology. With more than 30 million people accessing the Internet, more than 15 million smartphone users, there is a great opportunity to exploit Big Data to enhance economic and social activities in this country. France is a developed country, the sixth-largest economy in the world, with a gross domestic product of about 1.7 trillion euros. The application of Big Data in the financial and banking sector has brought tremendous and practical benefits. This article presents the study and application of Big Data in banking sector in France and proposes some lessons for Vietnam. Keywords: Big Data; 5Vs Mod; Information Value; Banking; Cloud Computing In France, similarly to other developed countries, the study of Big Data has been very well developed. Apart from some relevant scientific and management journals, the French scientific community has its own French-language Journal devoted to Big Data and a cloud computing called Lebidata.fr. On Big Data and Gartner's "5Vs" model, studies [1,5,7] show that the world is witnessing the explosion of the Big Data era with more than 90% of the world's data has been created for the last two years thanks to the rapid growth of Internet of Things (IoT) and mobile devices. Big Data is the collection of very huge and complex data which traditional data processing methods cannot handle. Studies also point out the difficulties in analyzing, collecting, monitoring, searching, sharing, storing, transmitting, visualizing, retrieving, and the privacy of data. In regard to the superiority of the Big Data solution, studies [5, 7] suggested that with great potential, Big Data is not only applied in business but also capable of affecting all most every sectors of the economy. Governments can apply Big Data to forecast unemployment rate or employment trends in the future. In other words, the application of Big Data will be a pre-eminent solution to promote socio-economic development 1

7 BIG DATA FOR BANKING AND FINANCIAL INDUSTRY - BDBF 2017 Figure 1- Data lifecycle in Big Data (Source: Bouazza N.B, 2017) Big Data's architecture is an issue drawn attention from many researchers [1,9,10]. Researcher Naoufal Ben Bouazza [2] has released the four major architectures of Big Data: Lambda architecture, Kappa architecture, HDFS architecture and HBase architecture. Technical solutions used in these architectures are based on Hadoop. Hadoop is a Java-based open source framework that supports the processing and storage of large datasets in a distributed computing environment. Hadoop, the basis for processing Big Data, allows running applications on multiple node systems and managing thousands of terabytes of data. Figure 2- Global Hadoop Market from 2015 to 2021 (Source: Mouhssine Ahid, 2016) 2

8 A STUDY ON THE APPLICATION OF BIG DATA IN FRENCH BANKING INDUSTRY According to a recent study conducted by Research Zion, cited by Mouhssine Ahid in [9], Hadoop's global market is worth $ 5 billion in 2015 and it could reach $ 59 billion by 2021, with annual growth rate of 51% from 2016 to The rise of structured and unstructured data in large corporations and the need for processing these data are factors driving growth of the global Hadoop market. For archieving Big Data, studies [1, 9] showed that Big Data is creating many challenges in storing them. Recent storage systems, which were designed more than 20 years ago, no longer meet new requirements because they often use inefficient technology, which is easy to be clogged and complex to manage the data. Therefore, it is necessary to develop a more suitable storage method for Big Data. The requirements that new storage systems need to meet are large-scale storage, the ability to support large data warehouse as a global data resource; the ability to expand easily; the ability to automatically store and back up data. For processing Big Data, studies [9, 10, 12, 14] mentioned two processing technologies used in Big Data: Batch processing and Stream processing. Batch processing is used for processing large volumes of data. Hadoop is one of the most common technologies for this process. The Hadoop platform provides users with two main components, the Hadoop Distributed File System (HDFS) and the MapReduce programming model. In the Stream processing, data is generated and transmitted continuously, processed in a very short time to meet the real time of the data. Stream processing technology is being studied and developed. One of the Stream processing models is Complex Event Processing (CEP), which treats streams of information as event notifications that need to be aggregated and combined to generate higher level events. With regards to Big Data and artificial intelligence, studies [2, 4] show the close link between Big Data and artificial intelligence. Nowadays, artificial intelligence has become very popular in businesses in all spheres of our society, in which decisions are made by smart machines. The need for smarter decisions in using Big Data will be the criteria that drive the technology trends in the future. 3 Along with academic studies, scientific seminars on Big Data and data mining are regularly organized in France. Even in 2017 there will be 3 large scale scientific conferences on the subject. - The CORIA 2017 Scientific Conference- This is the official conference of the Francophone community, sponsored by the Francophone Association for Information and Application, which was held from March 29th to 31 st in the port city of Marseille in France. The CORIA 2017 Scientific conference covers topics: Theory and Models for IR (Logical Models, Probability Models, Machine- Based Models); IR in mobile environments; Machine learning method; Exploit information (ontology, resources for IR); Web analytics (big chart, web topology); Digital library; IR specialities (search for information in gene, geography, health, patent and product); IR tools (evaluation, collection check, data check, qualitative assessment, open source assessmen) + The 4th International Scientific Conference on Big Data Analysis and Data Mining which will open September 7, 2017 in Paris, the French capital. The topic of the conference is the future technologies used for exploring knowledge in Big Data. The conference will cover key topics such as data mining applications in science, engineering, health care and medicine; Data mining methods and algorithms; Artificial intelligence; Data warehouse; Data mining tools and software; Big Data applications; Big Data algorithm; Big Data technology; Data mining analysis; Cloud computing; Social network analysis; Complexity and algorithms; Optimization and Big Data; Forecasts from Big Data; New display technique. + The 19th International Scientific Conference on Large Data Analysis and Knowledge Discovery - DaWaK 2017 will be opened in Lyon later this year. Theme 1- Academic Studies: Big Data query languages and optimization; Analysis of Big Data UI; Big Data storage; Big Data analysis (algorithms, techniques and systems); Big Data Search and exploration; Big Data management for mobile applications; Analysis of unstructured, structured and semi-structured data; Semantics for smart data; Analysis of data streams and sensor data; Analysis of multimedia data; Design and deployment of Big Data applications; Data warehousing and data

9 BIG DATA FOR BANKING AND FINANCIAL INDUSTRY - BDBF 2017 mining; Language and interface for data mining; Replication of Big Data experiments. Theme 2- Applied Researches: Big Data Analyzer and Knowledge Mining Tools; Big Data deployment: Experience in the industry; Application of Big Data in science, management, health, biology, etc.; Application of Big Data in intrusion detection / fraud detection; Application of Big Data in Smart business; Application of Big Data in Business management. According to Abdessatar Hammedi, an analytical CRM analyst at Lyon Credit Suisse - LCL, "only 20% of the data is structured in such a way that it can be stored and operated by a standard database management system (DBMS). Big Data will treat the data as a whole and see the truths behind the 80% unstructured data. " In the Banking system in France, Big Data is applied in the following areas [3, 6, 8, 11, 13]: + Application of Big Data for anti-money laundering In the era of globalization, banks often have to conduct a check on a client whether he or she is on the international sanctions list or not. However, the numbers of those whose name are the same are very large. It is the reason why banks must use Big Data to store and analyze customer data from a variety of sources such as their nationality, address, friends, relatives, and their immigration to restricted countries, etc. The information provided by Big Data will help the banks address well with this problem. + Application of Big Data in fighting against fraud and forgery Big Data allows detecting fraudulent acts in payment Bank. For all card payments, a study made by Abdessatar Hammedi [8] determines that the number 500 million by Using Big Data with the ability of collecting a very large and varied amount of data per customer will allow Banks to detect fraudulent in Bank transactions. + Application of Big Data in analyzing predictive 4 Facing with the increasing inefficiencies of traditional marketing methods, prediction marketing method is one of the levers of of banking development. The use of Big Data to analyze consumer behavior through banking and nonbanking consumers will allow banks to predict customers' trends. The extraction of unstructured data in Big Data will provide banks with complete and accurate information to predict behaviors of their clients in the future. + Application of Big Data for serving customers better The application of Big Data in banks will improve the quality of services to customers. Thanks to Big Data, banks will be able to clearly understand their customers' needs, habits, and hobbies, in order to improve their relationships with customers, establish proximity to customers and respond to their needs better. The pace of socio-economic development and integration with technological trends in Vietnam is more and more increasing. With more than 30 million Internet users, more than 15 million Mobile Internet users, Vietnam is having a big opportunity in exploiting Big Data. Along with this, contentbased services on the telecommunications and social media platforms are contributing more than 80% to means of communication like communicating online, online video as well as mobile digital content. This will create a huge Big Data storage in Vietnam. By studying the application of Big Data in banking industry in France, we have drawn some experience lessons for applying Big Data in Vietnam as follows: First of all, it is necessary for all of the top Leaders in the Banking sector in Vietnam to have the right perception of Big Data. The application of Big Data in Banking is not only a temporary trend but must be considered as an indispensable trend in the current stage of development and integration. Once you have the right perception, you must have the final determination to apply Big Data to the practice of your industry, your agency. The person who make

10 A STUDY ON THE APPLICATION OF BIG DATA IN FRENCH BANKING INDUSTRY the final decision for everything is the Leader. The fact that has been proved in France is that decision makers are the most decisive factors in the successful application of Big Data in the Banking industry. Secondly, there should be adequate investment in Big Data application project in Banks. The application of Big Data in Banks requires a satisfactory investment in IT infrastructure. In order to deploy Big Data, Banks need to invest in both Hardware and Software because Big Data is a huge and complex data set that is far beyond processing abilities of a traditional PC. A modern information technology platform will have capabilities of being storage, processing and analyzing in order to make accurate assessments and conclusions from this huge data storage. Thirdly, it is necessary to train well qualified human resources for research and application of Big Data in Vietnam. At the moment, there have not been a team of capable professionals with deep and systematical trained in applying Big Data in Vietnam. Even in training programs of IT majors or Management Information System of many universities, there have not been any courses of Big Data designed for teaching and learning. As a result, it is vital to focus on training special labour sources in applying Big Data. This is a matter of special importance and a key to all success in science and technology applications. The next key issue is to improve the legal corridor, policies for the collection and exploitation of Big Data formed from the Internet environment, social networking, video, audio... Banks always have to process millions of data from customer records, exchanged s, history of bank transactions, customers' feedbacks, diaries of transactions and calls... every day. These are huge Big Data storages. Most of them are unstructured data. As a result, it is impossible to use traditional structured data processing tools. The efficient exploitation of Big Data storage will bring great benefits to banks. Thanks to Big Data, banks can get a full vision of their customers by gathering their information to understand the needs of their clients to serve them better. However, this can be considered as a 5 violation of the privacy of their customers. Therefore the completion of the legal corridor is extremely necessary. The last key point is to research and deploy Big Data application in the needs of customers to marketing directly better to individuals. Mobile Banking services are growing rapidly in Vietnam. Through mobile banking, banks collect a lot of data about their customers. Analyzing this Big Data storage can help banks understand more clearly about the wants and needs of their customers. These knowledge allow Banks to offer suitable banking products to each client base on their specific conditions, habits and income levels. Besides, banks can use Big Data application in detecting money laundering and preventing fraudulent banking activities. Banking industry plays an important role in Vietnam economy in the current stage of development and integration. Due to its special features, banking is one of the first sectors to apply IT applications into business operations. At present and in the future, Big Data is an opportunity and a challenge for the whole Banking system of Vietnam. Having the correct and consistent awareness of the issue, investing adequately and satisfyingly in IT infrastructure, improving and perfecting the legal corridor, studying and applying the experience drawn from developed countries are orientations to help Vietnam banking sector to continue to develop and integrate. [1] Bastien L (2017) Hadoop - Tout savoir sur la principale plateforme Big Data - LeBigData.fr, [2] Bastien. L (2017), Intelligence artificielle et Big Data : la convergence de deux technologies révolutionnaires, [3] Bernard Marr, (2016), Big Data In Banking: How Citibank Delivers Real Business Benefits With Its Data-First Approach, [4] Big Data et Intelligence artificielle, (2016),

11 BIG DATA FOR BANKING AND FINANCIAL INDUSTRY - BDBF 2017 [5] Big Data: définition, enjeux et applications - LCL.com, [6] David Silverman, (2014), Big Data In Banking sector, www. Books.google.com.vn [7] Fabric Peudennier, (2015), Qu'est-ce que le Big Data?, [8] Hammedi. Data dans la banque, InformatiqueNe.fr [9] Mouhssine Ahid, (2016), Qu'est-ce que Hadoop? SUPINFO, École Supérieure d'informatique, [10] Naoufal Ben Bouazza, (2017), Comment faire le choix d'une architecture Big Data? [11] Steve. B, (2014), Big Data dans les banques: et nouveau sa banque?, exchange.fr [12] Sylvie Jolly, (2014), Avec Storm et YARN, Pentaho fournit une analyse des Big Data en, [13] Quels sont les exemples de projets de Big Data réussis dans la banque, [14] Raphaël Cornago, (2016), Hadoop: Big Data, une solution sort du lot - Digitools.io AUTHORS Han Viet Thuan Year of birth: University graduation: Bacu National University (previous Soviet Union) in PhD graduation: MECI University, Moscow, Russia in Year of getting Associate Professor: 2002, senior lecturer in Career title: Senior lecturer, former Head of Faculty of Economic Informatics. Office: Faculty of Economic Informatics, National Economics University. Research areas: Management information systems, software engineering, data structures and algorithms. Mobile phone: Phan Thanh Duc is working as a senior lecture and is the Dean of Faculty of MIS, Banking Academy, State Bank of Vietnam. He received B.Sc in Computer Science; M.Sc in CSIM from School of Advanced Technology, Asian Istitute of Technology, Thailand; Ph.D in Management Information Systems from National Economic University, Vietnam. His research interests include e-learning systems, business process management, electronic commerce and Big Data. He has published several papers in national and international refereed journals and conferences 6

12 International Conference, BDBF 2017 Hanoi, Vietnam, June 15 th 2017 Proceedings Responsible for publishing: Editor: Book Designer: Cover Designer: Director & Editor-in-Chief Pham Ngoc Khoi Nguyen Quynh Anh Nguyen Minh Chau Dang Nguyen Vu SCIENCE AND TECHNICS PUBLISHING HOUSE 70 Tran Hung Dao Street - Hanoi Tel: Fax: Website: BRANCH OFFICE nxbkhkt@hn.vnn.vn 28 Dong Khoi - District 1 - Ho Chi Minh City Tel: Quantity: 250 copies, size 19 x 24 cm at Photocopying and Printing Word Co., Ltd. Address: No. 87, Tran Dai nghia Street, Bach Khoa Ward, Hai Ba trung District, Ha noi City. Publishing license no: /CXBIPH/1-65/KHKT. Publishing decision no: 66/QDXB-NXBKHKT, date 14/6/2017. Printing completed and copies deposited in June ISBN: