{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:29:25Z","timestamp":1774456165347,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17681-z","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T05:02:19Z","timestamp":1702530139000},"page":"56939-56964","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A semi-supervised Anti-Fraud model based on integrated XGBoost and BiGRU with self-attention network: an application to internet loan fraud detection"],"prefix":"10.1007","volume":"83","author":[{"given":"Venkata Lakshmi Narayana","family":"Gorle","sequence":"first","affiliation":[]},{"given":"Suvasini","family":"Panigrahi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"17681_CR1","doi-asserted-by":"publisher","first-page":"100402","DOI":"10.1016\/j.cosrev.2021.100402","volume":"40","author":"KG Al-Hashedi","year":"2021","unstructured":"Al-Hashedi KG, Magalingam P (2021) Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019. Comput Sci Rev 40:100402","journal-title":"Comput Sci Rev"},{"key":"17681_CR2","doi-asserted-by":"crossref","unstructured":"Wang D, Lin J, Cui P, Jia Q, Wang Z, Fang Y, Yu Q, Zhou J, Yang S, Qi Y (2019) A semi-supervised graph attentive network for financial fraud detection. In IEEE International Conference on Data Mining (ICDM), Beijing, China, pp 598\u2013607","DOI":"10.1109\/ICDM.2019.00070"},{"key":"17681_CR3","doi-asserted-by":"publisher","first-page":"19161","DOI":"10.1109\/ACCESS.2018.2816564","volume":"6","author":"D Huang","year":"2018","unstructured":"Huang D, Mu D, Yang L, Cai X (2018) CoDetect: financial fraud detection with anomaly feature detection. IEEE Access 6:19161\u201319174","journal-title":"IEEE Access"},{"issue":"1","key":"17681_CR4","first-page":"39","volume":"45","author":"K Chaudhary","year":"2012","unstructured":"Chaudhary K, Yadav J, Mallick B (2012) A review of fraud detection techniques: credit card. Int J Comput Appl 45(1):39\u201344","journal-title":"Int J Comput Appl"},{"issue":"3","key":"17681_CR5","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.3233\/JIFS-169944","volume":"36","author":"SK Majhi","year":"2019","unstructured":"Majhi SK, Bhatachharya S, Pradhan R, Biswal S (2019) Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection. J Intell Fuzzy Syst 36(3):2333\u20132344","journal-title":"J Intell Fuzzy Syst"},{"key":"17681_CR6","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1016\/j.promfg.2020.05.012","volume":"46","author":"N Rtayli","year":"2020","unstructured":"Rtayli N, Enneya N (2020) Selection features and support vector machine for credit card risk identification. Procedia Manuf 46:941\u2013948","journal-title":"Procedia Manuf"},{"key":"17681_CR7","first-page":"1503","volume":"13","author":"F Itoo","year":"2021","unstructured":"Itoo F, Meenakshi, Singh S (2021) Comparison and analysis of logistic regression, Na\u00efve Bayes and KNN machine learning algorithms for credit card fraud detection. Int J Inf Technol 13:1503\u20131511","journal-title":"Int J Inf Technol"},{"issue":"2","key":"17681_CR8","first-page":"1","volume":"16","author":"I Ajah","year":"1970","unstructured":"Ajah I, Inyiama C (1970) Loan fraud detection and IT-based combat strategies. J Internet Bank Commer 16(2):1\u20133","journal-title":"J Internet Bank Commer"},{"key":"17681_CR9","doi-asserted-by":"publisher","first-page":"105936","DOI":"10.1016\/j.asoc.2019.105936","volume":"86","author":"N Arora","year":"2020","unstructured":"Arora N, Kaur PD (2020) A Bolasso based consistent feature selection enabled random forest classification algorithm: an application to credit risk assessment. Appl Soft Comput 86:105936","journal-title":"Appl Soft Comput"},{"key":"17681_CR10","doi-asserted-by":"crossref","unstructured":"Rahmawati D, Sarno R, Fatichah C, Sunaryono D (2017) Fraud detection on event log of bank financial credit business process using Hidden Markov Model algorithm. In IEEE 3rd International Conference on Science in Information Technology (ICSITech), Bandung, Indonesia, pp 35\u201340","DOI":"10.1109\/ICSITech.2017.8257082"},{"issue":"5","key":"17681_CR11","first-page":"141","volume":"13","author":"I Abiola","year":"2013","unstructured":"Abiola I, Oyewole AT (2013) Internal control system on fraud detection: Nigeria experience. J Account Financ 13(5):141\u2013152","journal-title":"J Account Financ"},{"issue":"19","key":"17681_CR12","doi-asserted-by":"publisher","first-page":"9637","DOI":"10.3390\/app12199637","volume":"12","author":"A Ali","year":"2022","unstructured":"Ali A, AbdRazak S, Othman SH, Eisa TA, Al-Dhaqm A, Nasser M, Elhassan T, Elshafie H, Saif A (2022) Financial fraud detection based on machine learning: a systematic literature review. Appl Sci 12(19):9637","journal-title":"Appl Sci"},{"key":"17681_CR13","doi-asserted-by":"crossref","unstructured":"Popat RR, Chaudhary J (2018) A survey on credit card fraud detection using machine learning. In IEEE 2nd international conference on trends in electronics and informatics (ICOEI), Tirunelveli, India, pp 1120\u20131125","DOI":"10.1109\/ICOEI.2018.8553963"},{"key":"17681_CR14","unstructured":"Nguyen TT, Tahir H, Abdelrazek M, Babar A (2020) Deep learning methods for credit card fraud detection. arXiv preprint arXiv:2012.03754"},{"key":"17681_CR15","unstructured":"Singla J (2020) A survey of deep learning based online transactions fraud detection systems. In IEEE International Conference on Intelligent Engineering and Management (ICIEM), London, UK, pp 130\u2013136"},{"key":"17681_CR16","doi-asserted-by":"crossref","unstructured":"Roy A, Sun J, Mahoney R, Alonzi L, Adams S, Beling P (2018) Deep learning detecting fraud in credit card transactions. In IEEE Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, pp 129\u2013134","DOI":"10.1109\/SIEDS.2018.8374722"},{"key":"17681_CR17","doi-asserted-by":"crossref","unstructured":"Abakarim Y, Lahby M, Attioui A (2018) An efficient real time model for credit card fraud detection based on deep learning. In: Proceedings of the 12th international conference on intelligent systems: theories and applications, Association for Computing Machinery, New York, NY, United States, pp 1\u20137","DOI":"10.1145\/3289402.3289530"},{"key":"17681_CR18","doi-asserted-by":"crossref","unstructured":"Mubalaike AM, Adali E (2018) Deep learning approach for intelligent financial fraud detection system. In IEEE 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina, pp 598\u2013603","DOI":"10.1109\/UBMK.2018.8566574"},{"key":"17681_CR19","doi-asserted-by":"publisher","first-page":"9777","DOI":"10.1109\/ACCESS.2021.3051079","volume":"9","author":"W Fang","year":"2021","unstructured":"Fang W, Li X, Zhou P, Yan J, Jiang D, Zhou T (2021) Deep learning anti-fraud model for internet loan: where we are going. IEEE Access 9:9777\u20139784","journal-title":"IEEE Access"},{"issue":"17","key":"17681_CR20","doi-asserted-by":"publisher","first-page":"9879","DOI":"10.3390\/su13179879","volume":"13","author":"CL Jan","year":"2021","unstructured":"Jan CL (2021) Detection of financial statement fraud using deep learning for sustainable development of capital markets under information asymmetry. Sustainability 13(17):9879","journal-title":"Sustainability"},{"key":"17681_CR21","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.ins.2019.05.023","volume":"557","author":"X Zhang","year":"2021","unstructured":"Zhang X, Han Y, Xu W, Wang Q (2021) HOBA: a novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Inf Sci 557:302\u2013316","journal-title":"Inf Sci"},{"issue":"1","key":"17681_CR22","first-page":"87","volume":"10","author":"F Baratzadeh","year":"2022","unstructured":"Baratzadeh F, Hasheminejad SM (2022) Customer behavior analysis to improve detection of fraudulent transactions using deep learning. J AI Data Min 10(1):87\u2013101","journal-title":"J AI Data Min"},{"issue":"5","key":"17681_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.9734\/jamcs\/2019\/v33i530192","volume":"33","author":"MA Al-Shabi","year":"2019","unstructured":"Al-Shabi MA (2019) Credit card fraud detection using autoencoder model in unbalanced datasets. J Adv Math Comput Sci 33(5):1\u20136","journal-title":"J Adv Math Comput Sci"},{"key":"17681_CR24","doi-asserted-by":"crossref","unstructured":"Yang W, Zhang Y, Ye K, Li L, Xu CZ (2019) Ffd: a federated learning based method for credit card fraud detection. InBig Data\u2013BigData 2019: 8th International Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25\u201330, 2019, Proceedings Springer International Publishing 8: 18-32","DOI":"10.1007\/978-3-030-23551-2_2"},{"key":"17681_CR25","doi-asserted-by":"crossref","unstructured":"Rushin G, Stancil C, Sun M, and Adams S, Beling P (2017) Horse race analysis in credit card fraud\u2014 deep learning, logistic regression, and Gradient Boosted Tree. In IEEE systems and information engineering design symposium (SIEDS), Charlottesville, VA, USA, pp 117\u2013121","DOI":"10.1109\/SIEDS.2017.7937700"},{"key":"17681_CR26","doi-asserted-by":"publisher","first-page":"113609","DOI":"10.1016\/j.cma.2020.113609","volume":"376","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Diabat A, Mirjalili S, AbdElaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"4","key":"17681_CR27","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.inffus.2008.04.001","volume":"10","author":"S Panigrahi","year":"2009","unstructured":"Panigrahi S, Kundu A, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using Dempster-Shafer theory and Bayesian learning. Inform Fusion 10(4):354\u2013363","journal-title":"Inform Fusion"},{"issue":"5","key":"17681_CR28","first-page":"568","volume":"32","author":"S Subudhi","year":"2020","unstructured":"Subudhi S, Panigrahi S (2020) Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. J King Saud Univ-Comput Inform Sci 32(5):568\u2013575","journal-title":"J King Saud Univ-Comput Inform Sci"},{"key":"17681_CR29","doi-asserted-by":"crossref","unstructured":"Karthika J, Senthilselvi A (2023) Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique. Multimed Tools Appl 82:31691\u201331708","DOI":"10.1007\/s11042-023-15730-1"},{"key":"17681_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00541-8","volume":"8","author":"I Benchaji","year":"2021","unstructured":"Benchaji I, Douzi S, El Ouahidi B, Jaafari J (2021) Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J Big Data 8:1\u201321","journal-title":"J Big Data"},{"issue":"2","key":"17681_CR31","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s00500-019-03958-9","volume":"24","author":"SM Darwish","year":"2020","unstructured":"Darwish SM (2020) An intelligent credit card fraud detection approach based on semantic fusion of two classifiers. Soft Comput 24(2):1243\u20131253","journal-title":"Soft Comput"},{"issue":"1","key":"17681_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-022-00573-8","volume":"9","author":"E Ileberi","year":"2022","unstructured":"Ileberi E, Sun Y, Wang Z (2022) A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data 9(1):1\u201317","journal-title":"J Big Data"},{"key":"17681_CR33","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.procs.2019.12.017","volume":"162","author":"L Zhu","year":"2019","unstructured":"Zhu L, Qiu D, Ergu D, Ying C, Liu K (2019) A study on predicting loan default based on the random forest algorithm. Procedia Comput Sci 162:503\u2013513","journal-title":"Procedia Comput Sci"},{"issue":"12","key":"17681_CR34","doi-asserted-by":"publisher","first-page":"14571","DOI":"10.1007\/s11227-022-04465-9","volume":"78","author":"G Zioviris","year":"2022","unstructured":"Zioviris G, Kolomvatsos K, Stamoulis G (2022) Credit card fraud detection using a deep learning multistage model. J Supercomput 78(12):14571\u201314596","journal-title":"J Supercomput"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17681-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17681-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17681-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T06:27:44Z","timestamp":1716618464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17681-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["17681"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17681-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"28 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All the authors involved have agreed to participate in this submitted article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors involved in this manuscript give full consent for publication of this submitted article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"Authors declare that they have no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}