在金融科技中運用大數據分析並不是全新的方法，事實上，已經施行多年。藉由運
用大數據方法過濾不相關的資訊，使得企業專注在有用的資訊上，以便於降低風險，並有助於提升公司正面的效益。另一方面，在工業 4.0 發展下，其中以製造業智慧化為主軸，主要結合物聯網、雲端運算以及大數據分析的應用。而本研究把主要焦點放在大數據分析中，所謂的大數據是指具有「快速」產生「多變」的「大量」資料等特性的數據。在大數據分析中，所分析的資料屬非結構化的資料，指的是沒有固定格式、沒有明確分析邏輯資料，例如：工業資料、金融據資料等等。值得注意的事，洗錢是最近非常熱門的議題，因此，各國政府以及金融監管機構要求相關金融單位實施預防洗錢和反恐怖主義的措施流程。
在此研究，我們設計出即時洗錢偵測系統，能在發生潛在異常交易的情況下，能立
即通知相關人員。設計此系統我們是利用金額大小的相對概念來判斷可能存在洗錢交易的行為。儘管我們所提出的系統效能並不是最好的，但所運行的時間是最短的，計算時間幾乎為即時，因為我們研究的主軸在於能快速的偵測出可能洗錢交易的行為。此系統具備區分正常資料以及異常資料的功能，以利於自我學習進而改善系統。為了要驗證此系統，本研究將以金融背景的個案銀行資料作為研究題材。我們也比較不同的智慧型演算法的效能以及運行時間。在未來，此系統不僅只侷限於此個案資料，也能應用於其他相關的資料。

Applying Big Data is not new in the FinTech world, in fact it has been around for years. By filtering irrelevant information, the enterprises are able to focus on useful information that reduces risk and identifies the possibility of positive returns. On the other hand, intelligent manufacturing industry focuses on the integration of Internet of Things, cloud computing and big data analytics in the Industry 4.0. In this study, we focused on the big data analytics, having three properties: velocity, variety and volume. In Big Data analytics, all of the data was unstructured data, which means that data with no fixed data structure and undefined logical relationships, such as industry data, financial data, etc.Especially, money laundering is one of a popular issue in recent years.Therefore, the governments and financial regulators require financial institutions to implement preventive measure processes for money laundering as well as the financing of terrorism.
In this research, we proposed a real time money laundering detection system to alarm the relative personnel in order to response to potential unusual transactions. We used the amount money concept of relativity to judge money laundering activity in this system. The proposed real-time system could take shortest execution time, almost real-time, despite of not the best performance focusing on detection possible money laundering transactions activity rapidly. The system was capable of classification normal transaction and unusual transaction so that it could self-learning to improve this system from classified dataset. We conducted big data analysis and used dataset from the case study in the field of financial institutions to verify the proposed system. Finally, we compared performance and execution time of the proposed real time system with different intelligent algorithms. Furthermore, this system not only used for this case data but also it can apply others relatively data.

1. Adeyeri, M. K., Mpofu, K. and Olukorede, A. T. (2015). Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0. The 2015 International Conference on Industrial Engineering and Operations Management, 1–10.
2. Box, G. and Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
3. Chien, C. F. and Hsu, C. Y. (2014). Data Mining & Big Data Analytics. Future Career Management Corporation.
4. Chu, F. L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740–751.
5. Demchenko, Y., De Laat, C. and Membrey, P. (2014). Defining architecture components of the big data ecosystem. The 2014 International Conference on Collaboration Technologies and Systems, 104–112.
6. Fritsch, S., Guenther, F., Suling, M. and Mueller, M. (2016). The Package Neuralnet, from https://cran.rproject.org/web/packages/neuralnet/neuralnet.pdf.
7. Gandomi, A. and Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
8. Gao, Z. (2009). Application of cluster-based local outlier factor algorithm in anti-money laundering. The 2009 International Conference on Management and Service Science, 1–4.
9. Gao, Z. A. and Weng, L. F. (2006). Transfer price-based money laundering in international trade. The 2006 International Conference on Management Science and Engineering, 1128–1132.
10. Garay, J. A. (2017). Basic Properties of the Block chain. Proceedings of the ACM Workshop on Block chain, 1–1.
11. Holley, K., Sivakumar, G. and Kannan, K. (2014). Enrichment patterns for big data. The 2014 IEEE International Congress on Big Data, 796–799.
12. Hong, X., Liang, H., Cai, L. X., Gao, Z. and Sun, L. (2015). Peer to peer anti-money laundering resource allocation based on semi-markov decision process. The 2015 IEEE Global Communications Conference, 1–6.
13. Iansiti, M. and Lakhani, K. R. (2014). Digital ubiquity: How connections, sensors, and data are revolutionizing business. Harvard Business Review, 92(11), 91–99.
14. Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. The 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, 1–4.
15. Kim, M., Zimmermann, T., DeLine, R., and Begel, A. (2016). The emerging role of data scientists on software development teams. Proceedings of the 38th International Conference on Software Engineering, 96–107.
16. Khac, N. A. L. and Kechadi, M. T. (2010). Application of data mining for anti-money laundering detection: A case study. The 2010 IEEE International Conference on Data Mining Workshops, 577–584.
17. Laouafi, A., Mordjaoui, M. and Dib, D. (2014). Very short-term electricity demand forecasting using adaptive exponential smoothing methods. The 2014 International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, 553–557.
18. Lee, J., Kao, H. A. and Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16, 3–8.
19. Li, M., Chen, G., Lin, Z. and Cai, B. (2009). Combination forecasting model based on drift. The Computational Sciences and Optimization. International Joint Conference, 2, 443–446.
20. Liu, R., Qian, X. L., Mao, S. and Zhu, S. Z. (2011). Research on anti-money laundering based on core decision tree algorithm. The 2011 Chinese Control and Decision Conference, 4322–4325.
21. Liu X. and Zhang, P. (2007). An agent based anti-money laundering system architecture for financial supervision. The 2007 International Conference on Wireless Communications, Networking and Mobile Computing, 5472–5475.
22. Lv, L. T., Ji, N. and Zhang, J. L. (2008). A RBF neural network model for anti-money laundering. The 2008 International Conference on Wavelet Analysis and Pattern Recognition, 1, 209–215.
23. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C. C. and Lin, C. C. (2017). The Package e1071, from
https://cran.r-project.org/web/packages/e1071/e1071.pdf
24. Munar, A., Chiner, E. and Sales, I. (2014). A big data financial information management architecture for global banking. The 2014 International Conference on Future Internet of Things and Cloud, 385–388.
25. Nicoletti, B. (2017). The Future of FinTech, Springer International Publishing, 81–159.
26. Nowotarski, J., Liu, B., Weron, R. and Hong, T. (2016). Improving short term load forecast accuracy via combining sister forecasts. Energy, 98, 40–49.
27. Parise, S., Iyer, B. and Vesset, D. (2012). Four strategies to capture and create value from big data. Ivey Business Journal, 76(4), 1–5.
28. Shrouf, F., Ordieres, J. and Miragliotta, G. (2014). Smart factories in industry 4.0: a review of the concept and of energy management approached in production based on the Internet of things paradigm. The 2014 IEEE International Conference on Industrial Engineering and Engineering Management, 697–701.
29. Stock, T. and Seliger, G. (2016). Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP, 40, 536–541.
30. Tapscott, D., and Tapscott, A. (2016). Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World. Penguin.
31. The R Core Team. (2017). The Package K-Means Clustering, from https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html
32. The R Foundation. (2017). An Introduction to R, from https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
33. Dapp, T. F. (2015). Fintech reloaded–Traditional banks as digital ecosystems with proven walled garden strategies into the future. Deutsche Bank Research, 1–26.
34. van der Walt, C. M. and Botha, N. (2016). A comparison of regression algorithms for wind speed forecasting at Alexander Bay. The Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, 1–5.
35. Varghese, A. and Tandur, D. (2014). Wireless requirements and challenges in Industry 4.0. The 2014 International Conference on Contemporary Computing and Informatics, 634–638.
36. Waller, M. A. and Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
37. Wang, S., Wan, J., Zhang, D., Li, D. and Zhang, C. (2016). Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158–168.
38. Wang, W. C., Chau, K. W., Qiu, L. and Chen, Y. B. (2015). Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. Environmental Research, 139, 46–54.
39. Wang, X. and Dong, G. (2009). Research on money laundering detection based on improved minimum spanning tree clustering and its application. The 2009 Second International Symposium on Knowledge Acquisition and Modeling, 62–64.
40. Wamba, S. F. (2017). Big data analytics and business process innovation, Business Process Management Journal, 23(3), https://doi.org/10.1108/BPMJ-02-2017-0046.
41. Yang, Y. and Yu, C. (2015). Prediction models based on multivariate statistical methods and their applications for predicting railway freight volume. Neurocomputing, 158, 210–215.
42. Yen, C. T., Liu, Y. C., Lin, C. C., Kao, C. C., Wang, W. B. and Hsu, Y. R. (2014). Advanced manufacturing solution to industry 4.0 trend through sensing network and Cloud Computing technologies. The 2014 IEEE International Conference on Automation Science and Engineering, 1150–1152.