{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:21:53Z","timestamp":1783610513875,"version":"3.55.0"},"reference-count":130,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore, and Infocomm Media Development Authority under its Trust Tech Funding Initiative, Seatrium New Energy Laboratory, Singapore, Ministry of Education (MOE) Tier 1","doi-asserted-by":"publisher","award":["RT5\/23"],"award-info":[{"award-number":["RT5\/23"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore, and Infocomm Media Development Authority under its Trust Tech Funding Initiative, Seatrium New Energy Laboratory, Singapore, Ministry of Education (MOE) Tier 1","doi-asserted-by":"publisher","award":["RG24\/24"],"award-info":[{"award-number":["RG24\/24"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nanyang Technological University (NTU) Centre for Computational Technologies in Finance"},{"name":"Research Innovation and Enterprise (RIE) 2025 Industry Alignment Fund\u2014Industry Collaboration Projects (IAF-ICP) administered by Agency for Science, Technology and Research","award":["I2301E0026"],"award-info":[{"award-number":["I2301E0026"]}]},{"name":"Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2026,3,15]]},"DOI":"10.1109\/jiot.2026.3653434","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:02:44Z","timestamp":1768255364000},"page":"10407-10431","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9591-5996","authenticated-orcid":false,"given":"Jiani","family":"Fan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5130-0407","authenticated-orcid":false,"given":"Lwin","family":"Khin Shar","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Bras Basah, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6859-3645","authenticated-orcid":false,"given":"Ruichen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4060-0839","authenticated-orcid":false,"given":"Ziyao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhuo","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-7416","authenticated-orcid":false,"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7479-7970","authenticated-orcid":false,"given":"Kwok-Yan","family":"Lam","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1023\/a:1008776629651"},{"key":"ref2","volume-title":"Money Laundering-Overview","year":"2025"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1300"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.4337\/9781849806657","volume-title":"Technology and Anti-Money Laundering: A Systems Theory and Risk-Based Approach","author":"Demetis","year":"2010"},{"key":"ref5","volume-title":"Virtual Assets: Red Flag Indicators","year":"2022"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3391767"},{"issue":"1","key":"ref7","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.ijar.2006.01.004","article-title":"Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning","volume":"44","author":"Ishibuchi","year":"2007","journal-title":"Int. J. Approx. Reasoning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2015.09.005"},{"key":"ref9","first-page":"4","article-title":"A model for identifying relationships of suspicious customers in money laundering using social network functions","volume-title":"Proc. World Congr. Eng.","author":"Shaikh"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2016.0172"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113318","article-title":"Detection of illicit accounts over the Ethereum blockchain","volume":"150","author":"Farrugia","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3252040"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1144-z"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3086230"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.0131087"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-27762-7_8"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3239549"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.26512\/lstr.v13i2.37425","volume-title":"General Data Protection Regulation (GDPR)","author":"Guerra","year":"2021"},{"key":"ref19","volume-title":"Personal Information Protection Law of the People\u2019s Republic of China (PIPL)","year":"2022"},{"key":"ref20","volume-title":"The Bank Secrecy Act","year":"2022"},{"key":"ref21","volume-title":"Banking Act 1970","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/373256.373257"},{"key":"ref23","volume-title":"Group Structure","year":"2023"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3346276"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/9781119208969"},{"key":"ref26","first-page":"133","article-title":"Rule-based anti-money laundering in financial intelligence units: Experience and vision","volume":"2644","author":"Bellomarini","year":"2020","journal-title":"RuleML+ RR (Supplement)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3501361"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1108\/jfc-10-2016-0063"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1108\/jmlc-05-2015-0018"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116429"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3426955"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1002\/isaf.1509"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1108\/JFC-09-2022-0227"},{"key":"ref34","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106384","article-title":"Deep learning for financial applications: A survey","volume":"93","author":"Ozbayoglu","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3380262"},{"key":"ref36","volume-title":"Elliptic Dataset: Cryptocurrency and Financial Crime","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-021-06116-2"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3416733"},{"key":"ref39","first-page":"37","article-title":"NextGen AML: Distributed deep learning based language technologies to augment anti money laundering investigation","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics","author":"Han"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3098051"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3161050"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-34671-2_17"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2024.3459037"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.016.2300547"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3040957"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/LA-CCI62337.2024.10814854"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT61001.2024.10724384"},{"issue":"3","key":"ref48","doi-asserted-by":"crossref","first-page":"378","DOI":"10.3390\/sym16030378","article-title":"A novel money laundering prediction model based on a dynamic graph convolutional neural network and long short-term memory","volume":"16","author":"Wan","year":"2024","journal-title":"Symmetry"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-024-10791-2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3086359"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.2478\/ijssis-2024-0027"},{"key":"ref52","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"arXiv:1705.07874"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/MLBDBI63974.2024.10823769"},{"key":"ref54","article-title":"Topology adaptive graph convolutional networks","author":"Du","year":"2017","journal-title":"arXiv:1710.10370"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-022-10904-8"},{"key":"ref60","article-title":"Wasserstein GAN","author":"Arjovsky","year":"2017","journal-title":"arXiv:1701.07875"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3401159"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-7356-9_39"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3677052.3698648"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3167699"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.29069"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1145\/3533271.3561727"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3409073.3409080"},{"key":"ref69","article-title":"Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics","author":"Weber","year":"2019","journal-title":"arXiv:1908.02591"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04504-9"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-33377-4_10"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3049278"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3272396"},{"key":"ref74","article-title":"GuiltyWalker: Distance to illicit nodes in the Bitcoin network","author":"Oliveira","year":"2021","journal-title":"arXiv:2102.05373"},{"key":"ref75","first-page":"3149","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Ke"},{"key":"ref76","article-title":"Deep graph infomax","author":"Veli\u010dkovi\u0107","year":"2018","journal-title":"arXiv:1809.10341"},{"key":"ref77","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018","journal-title":"arXiv:1810.00826"},{"key":"ref78","article-title":"Strategies for pre-training graph neural networks","author":"Hu","year":"2019","journal-title":"arXiv:1905.12265"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref80","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Velikovi"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449989"},{"key":"ref82","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017","journal-title":"arXiv:1706.02216"},{"key":"ref83","first-page":"28877","article-title":"Do transformers really perform badly for graph representation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Ying"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/tse.2024.3385538"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3322270"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/INTECH.2017.8102446"},{"key":"ref87","article-title":"Application of deep reinforcement learning to payment fraud","author":"Vimal","year":"2021","journal-title":"arXiv:2112.04236"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2015.33"},{"key":"ref90","volume-title":"IEEE-CIS Fraud Detection Competition","year":"2019"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00592-x"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa8415"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.1996.11518099"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.23919\/JSC.2022.0011"},{"key":"ref95","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.future.2024.05.027","article-title":"Transaction monitoring in anti-money laundering: A qualitative analysis and points of view from industry","volume":"159","author":"Oztas","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0192-5"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3383784"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1145\/3209950.3209952"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3387741"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5422-z"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3170449"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/ICIDCA56705.2023.10099859"},{"key":"ref103","volume-title":"California Consumer Privacy Act (CCPA)","year":"2024"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3190835"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1108\/JMLC-03-2020-0029"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1093\/bjc\/azh019"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ICEBE59045.2023.00024"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10050593"},{"issue":"19","key":"ref109","doi-asserted-by":"crossref","first-page":"9637","DOI":"10.3390\/app12199637","article-title":"Financial fraud detection based on machine learning: A systematic literature review","volume":"12","author":"Ali","year":"2022","journal-title":"Appl. Sci."},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10021108"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3394528"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3457577"},{"issue":"6","key":"ref114","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.cose.2009.02.001","article-title":"A survey of signature based methods for financial fraud detection","volume":"28","author":"Edge","year":"2009","journal-title":"Comput. Secur."},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2482465"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2021.3052950"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/SIEDS.2019.8735623"},{"key":"ref118","volume-title":"Countries-FATF-Financial Action Task Force","year":"2024"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3052313"},{"key":"ref120","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018","journal-title":"arXiv:1806.01261"},{"issue":"12","key":"ref121","first-page":"10737","article-title":"Identity-aware graph neural networks","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"35","author":"You"},{"key":"ref122","article-title":"Approximation ratios of graph neural networks for combinatorial problems","author":"Sato","year":"2019","journal-title":"arXiv:1905.10261"},{"key":"ref123","article-title":"EdGNN: A simple and powerful GNN for directed labeled graphs","author":"Jaume","year":"2019","journal-title":"arXiv:1904.08745"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"issue":"15","key":"ref125","doi-asserted-by":"crossref","first-page":"8766","DOI":"10.3390\/app13158766","article-title":"HBTBD: A heterogeneous Bitcoin transaction behavior dataset for anti-money laundering","volume":"13","author":"Song","year":"2023","journal-title":"Appl. Sci."},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12454"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11424034\/11346961.pdf?arnumber=11346961","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T20:01:15Z","timestamp":1773086475000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11346961\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,15]]},"references-count":130,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2026.3653434","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,15]]}}}