{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T11:24:53Z","timestamp":1783250693259,"version":"3.54.6"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031277610","type":"print"},{"value":"9783031277627","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-27762-7_8","type":"book-chapter","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T12:58:58Z","timestamp":1677761938000},"page":"77-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["State of the Art Literature on Anti-money Laundering Using Machine Learning and Deep Learning Techniques"],"prefix":"10.1007","author":[{"given":"Bekach","family":"Youssef","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frikh","family":"Bouchra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ouhbi","family":"Brahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Kute, D.V., et al.: Deep learning and explainable artificial intelligence techniques applied for detecting money laundering\u2013a critical review. IEEE Access (2021)","DOI":"10.1109\/ACCESS.2021.3086230"},{"issue":"20","key":"8_CR2","first-page":"10084","volume":"12","author":"A Salehi","year":"2017","unstructured":"Salehi, A., Ghazanfari, M., Fathian, M.: Data mining techniques for anti money laundering. Int. J. Appl. Eng. Res. 12(20), 10084\u201310094 (2017)","journal-title":"Int. J. Appl. Eng. Res."},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"18481","DOI":"10.1109\/ACCESS.2021.3052313","volume":"9","author":"M Alkhalili","year":"2021","unstructured":"Alkhalili, M., Qutqut, M.H., Almasalha, F.: Investigation of applying machine learning for watch-list filtering in anti-money laundering. IEEE Access 9, 18481\u201318496 (2021)","journal-title":"iEEE Access"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Ketenci, U.G., et al.: A time-frequency based suspicious activity detection for anti-money laundering. IEEE Access 9, 59957\u201359967 (2021)","DOI":"10.1109\/ACCESS.2021.3072114"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-S\u00e1nchez, J.F., Cruz-Garc\u00eda, S., Venegas-Mart\u00ednez, F.: Money laundering control in Mexico: a risk management approach through regression trees (data mining). J. Money Laundering Control (2020)","DOI":"10.1108\/JMLC-10-2019-0083"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Jullum, M., L\u00f8land, A., Huseby, R.B., \u00c5nonsen, G., Lorentzen, J.: Detecting money laundering transactions with machine learning. J. Money Laundering Control (2020)","DOI":"10.1108\/JMLC-07-2019-0055"},{"issue":"3","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1007\/s10614-018-9864-z","volume":"54","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Trubey, P.: Machine learning and sampling scheme: an empirical study of money laundering detection. Comput. Econ. 54(3), 1043\u20131063 (2019)","journal-title":"Comput. Econ."},{"key":"8_CR8","unstructured":"Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Colladon, A.F., Remondi, E.: Using social network analysis to prevent money laundering. Expert Syst. Appl. 67, 49\u201358 (2017)","DOI":"10.1016\/j.eswa.2016.09.029"},{"key":"8_CR10","unstructured":"Shokry, A.E.M., Rizka, M.A., Labib, N.M.: Counter terrorism finance by detecting money laundering hidden networks using unsupervised machine learning algorithm. In: International Conferences ICT, Society, and Human Beings (2020)"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Flowscope: spotting money laundering based on graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 4731\u20134738 (2020)","DOI":"10.1609\/aaai.v34i04.5906"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Paula, E.L., et al.: Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE (2016)","DOI":"10.1109\/ICMLA.2016.0172"},{"key":"8_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114470","volume":"169","author":"M-J Segovia-Vargas","year":"2021","unstructured":"Segovia-Vargas, M.-J.: Money laundering and terrorism financing detection using neural networks and an abnormality indicator. Expert Syst. Appl. 169, 114470 (2021)","journal-title":"Expert Syst. Appl."},{"key":"8_CR14","unstructured":"Ruiz, E.P., Angelis, J.: Combating money laundering with machine learning\u2013applicability of supervised-learning algorithms at cryptocurrency exchanges. J. Money Laundering Control (2021)"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Harris, D.A., et al.: Using real-world transaction data to identify money laundering: Leveraging traditional regression and machine learning techniques. STEM Fellowship J. 7(1), 1\u201311 (2021)","DOI":"10.17975\/sfj-2021-006"},{"key":"8_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2019.06.001","volume":"34","author":"K Singh","year":"2019","unstructured":"Singh, K., Best, P.: Anti-money laundering: using data visualization to identify suspicious activity. Int. J. Account. Inf. Syst. 34, 100418 (2019)","journal-title":"Int. J. Account. Inf. Syst."},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Jensen, R.I.T., Iosifidis, A.: Qualifying and raising anti-money laundering alarms with deep learning. Expert Syst. Appl. 214, 119037 (2023)","DOI":"10.1016\/j.eswa.2022.119037"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"3217","DOI":"10.1016\/j.procs.2022.09.379","volume":"207","author":"C-Y Lin","year":"2022","unstructured":"Lin, C.-Y., Liao, H.-K., Tsai, F.-C.: A systematic review of detecting illicit bitcoin transactions. Procedia Comput. Sci. 207, 3217\u20133225 (2022)","journal-title":"Procedia Comput. Sci."},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Tertychnyi, P., et al.: Time-aware and interpretable predictive monitoring system for anti-money laundering.\u00a0Mach. Learn. Appl.\u00a08, 100306 (2022)","DOI":"10.1016\/j.mlwa.2022.100306"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Han, J., et al.: Artificial intelligence for anti-money laundering: a review and extension. Digit. Finance\u00a02(3), 211\u2013239 (2020)","DOI":"10.1007\/s42521-020-00023-1"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Alarab, I., Prakoonwit, S., Nacer, M.I.: Competence of graph convolutional networks for anti-money laundering in bitcoin blockchain.\u00a0In: Proceedings of the 2020 5th International Conference on Machine Learning Technologies (2020)","DOI":"10.1145\/3409073.3409080"},{"key":"8_CR22","unstructured":"Hu, Y., et al.: Characterizing and detecting money laundering activities on the bitcoin network. arXiv preprint arXiv:1912.12060(2019)"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Lorenz, J., et al.: Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In:\u00a0Proceedings of the First ACM International Conference on AI in Finance (2020)","DOI":"10.1145\/3383455.3422549"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5\u20137, 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27762-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T12:59:26Z","timestamp":1677761966000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-27762-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031277610","9783031277627"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-27762-7_8","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"value":"2367-4512","type":"print"},{"value":"2367-4520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AICV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Artificial Intelligence and Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 March 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 March 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aicv12023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/egyptscience.net\/AICV2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}