{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:36:06Z","timestamp":1772908566741,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031637995","type":"print"},{"value":"9783031638008","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-63800-8_18","type":"book-chapter","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T23:03:55Z","timestamp":1720566235000},"page":"350-373","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Explainable Fraud Detection with\u00a0Deep Symbolic Classification"],"prefix":"10.1007","author":[{"given":"Samantha","family":"Visbeek","sequence":"first","affiliation":[]},{"given":"Erman","family":"Acar","sequence":"additional","affiliation":[]},{"given":"Floris","family":"den Hengst","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"39700","DOI":"10.1109\/ACCESS.2022.3166891","volume":"10","author":"FK Alarfaj","year":"2022","unstructured":"Alarfaj, F.K., Malik, I., Khan, H.U., Almusallam, N., Ramzan, M., Ahmed, M.: Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access 10, 39700\u201339715 (2022)","journal-title":"IEEE Access"},{"key":"18_CR2","unstructured":"Alvarez-Melis, D., Jaakkola, T.S.: On the robustness of interpretability methods (June 2018)"},{"key":"18_CR3","volume":"6","author":"M Aria","year":"2021","unstructured":"Aria, M., Cuccurullo, C., Gnasso, A.: A comparison among interpretative proposals for random forests. Mach. Learn. Appl. 6, 100094 (2021)","journal-title":"Mach. Learn. Appl."},{"key":"18_CR4","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.eswa.2015.12.030","volume":"51","author":"AC Bahnsen","year":"2016","unstructured":"Bahnsen, A.C., Aouada, D., Stojanovic, A., Ottersten, B.: Feature engineering strategies for credit card fraud detection. Expert Syst. Appl. 51, 134\u2013142 (2016)","journal-title":"Expert Syst. Appl."},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785\u2013794. ACM, New York, NY (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Dal\u00a0Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., Bontempi, G.: Credit card fraud detection and concept-drift adaptation with delayed supervised information. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE, iscataway, New Jersey (2015)","DOI":"10.1109\/IJCNN.2015.7280527"},{"key":"18_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-83213-1","volume-title":"Machine Learning Control by Symbolic Regression","author":"A Diveev","year":"2021","unstructured":"Diveev, A., Shmalko, E.: Machine Learning Control by Symbolic Regression. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-83213-1"},{"key":"18_CR8","unstructured":"Garreau, D., Luxburg, U.: Explaining the explainer: a first theoretical analysis of lime. In: International Conference on Artificial Intelligence and Statistics, pp. 1287\u20131296. Springer, Cham, Switzerland (2020)"},{"issue":"3","key":"18_CR9","first-page":"50","volume":"38","author":"B Goodman","year":"2017","unstructured":"Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a \u201cright to explanation\u2019\u2019. AI Mag. 38(3), 50\u201357 (2017)","journal-title":"AI Mag."},{"key":"18_CR10","first-page":"1","volume":"162","author":"P Hajek","year":"2022","unstructured":"Hajek, P., Abedin, M.Z., Sivarajah, U.: Fraud detection in mobile payment systems using an XGBoost-based framework. Inf. Syst. Front. 162, 1\u201319 (2022)","journal-title":"Inf. Syst. Front."},{"issue":"1","key":"18_CR11","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s40163-020-00119-4","volume":"9","author":"M Junger","year":"2020","unstructured":"Junger, M., Wang, V., Schl\u00f6mer, M.: Fraud against businesses both online and offline: crime scripts, business characteristics, efforts, and benefits. Crime Sci. 9(1), 13 (2020)","journal-title":"Crime Sci."},{"key":"18_CR12","first-page":"10269","volume":"35","author":"PA Kamienny","year":"2022","unstructured":"Kamienny, P.A., d\u2019Ascoli, S., Lample, G., Charton, F.: End-to-end symbolic regression with transformers. Proc. NeurIPS 35, 10269\u201310281 (2022)","journal-title":"Proc. NeurIPS"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.eswa.2019.03.042","volume":"128","author":"E Kim","year":"2019","unstructured":"Kim, E., et al.: Champion-challenger analysis for credit card fraud detection: hybrid ensemble and deep learning. Expert Syst. Appl. 128, 214\u2013224 (2019)","journal-title":"Expert Syst. Appl."},{"key":"18_CR14","volume-title":"Genetic Programming: On the Programming of Computers by Means of Natural Selection","author":"JR Koza","year":"1992","unstructured":"Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA (1992)"},{"key":"18_CR15","unstructured":"Kumar, I.E., Venkatasubramanian, S., Scheidegger, C., Friedler, S.: Problems with shapley-value-based explanations as feature importance measures. In: International Conference on Machine Learning, pp. 5491\u20135500. PMLR, Vienna, Austria (2020)"},{"key":"18_CR16","unstructured":"La\u00a0Cava, W., Orzechowski, P., Burlacu, B., de\u00a0Franca, F.O., Virgolin, M., Jin, Y., Kommenda, M., Moore, J.H.: Contemporary symbolic regression methods and their relative performance. In: Thirty-fifth Conference on Neural Information Processing Systems. PMLR, online (2021)"},{"key":"18_CR17","first-page":"33985","volume":"35","author":"M Landajuela","year":"2022","unstructured":"Landajuela, M., et al.: A unified framework for deep symbolic regression. Proc. NeurIPS 35, 33985\u201333998 (2022)","journal-title":"Proc. NeurIPS"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Liu, C., Arnon, T., Lazarus, C., Strong, C., Barrett, C., Kochenderfer, M.J., et\u00a0al.: Algorithms for verifying deep neural networks. Found. Trends\u00ae in Optimization 4(3\u20134), 244\u2013404 (2021)","DOI":"10.1561\/2400000035"},{"key":"18_CR19","unstructured":"Lopez-Rojas, E., Elmir, A., Axelsson, S.: PaySim: a financial mobile money simulator for fraud detection. In: 28th European Modeling and Simulation Symposium, EMSS, Larnaca, pp. 249\u2013255 (2016)"},{"key":"18_CR20","doi-asserted-by":"publisher","unstructured":"Mainali, P., Psychoula, I., Petitcolas, F.A.: ExMo: Explainable AI Model using inverse frequency decision rules. In: International Conference on Human-Computer Interaction, vol. 13336, pp. 179\u2013198. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-05643-7_12","DOI":"10.1007\/978-3-031-05643-7_12"},{"key":"18_CR21","unstructured":"Mundhenk, T.N., Landajuela, M., Glatt, R., Santiago, C.P., Faissol, D.M., Petersen, B.K.: Symbolic regression via neural-guided genetic programming population seeding (2021)"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Nesvijevskaia, A., Ouillade, S., Guilmin, P., Zucker, J.D.: The accuracy versus interpretability trade-off in fraud detection model. Data Policy 3, e12 (2021)","DOI":"10.1017\/dap.2021.3"},{"key":"18_CR23","unstructured":"Petersen, B.K., Larma, M.L., Mundhenk, T.N., Santiago, C.P., Kim, S.K., Kim, J.T.: Deep symbolic regression: recovering mathematical expressions from data via risk-seeking policy gradients. In: Proceedings of ICLR (2021)"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Raghavan, P., El\u00a0Gayar, N.: Fraud detection using machine learning and deep learning. In: 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 334\u2013339. IEEE (2019)","DOI":"10.1109\/ICCIKE47802.2019.9004231"},{"issue":"5","key":"18_CR25","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nature Mach. Intell."},{"key":"18_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","year":"2019","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.): Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Sipper, M.: Binary and multinomial classification through evolutionary symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 300\u2013303 (2022)","DOI":"10.1145\/3520304.3528922"},{"key":"18_CR28","doi-asserted-by":"publisher","unstructured":"Smits, G.F., Kotanchek, M.: Pareto-front exploitation in symbolic regression. Genetic programming theory and practice II, pp. 283\u2013299. Springer, Cham (2005). https:\/\/doi.org\/10.1007\/0-387-23254-0_17","DOI":"10.1007\/0-387-23254-0_17"},{"key":"18_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110866","volume":"278","author":"F Sovrano","year":"2023","unstructured":"Sovrano, F., Vitali, F.: An objective metric for explainable AI: how and why to estimate the degree of explainability. Knowl.-Based Syst. 278, 110866 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Sundarkumar, G.G., Ravi, V., Siddeshwar, V.: One-class support vector machine based undersampling: application to churn prediction and insurance fraud detection. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp.\u00a01\u20137. IEEE (2015)","DOI":"10.1109\/ICCIC.2015.7435726"},{"issue":"3","key":"18_CR31","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1089\/big.2016.0051","volume":"5","author":"KR Varshney","year":"2017","unstructured":"Varshney, K.R., Alemzadeh, H.: On the safety of machine learning: cyber-physical systems, decision sciences, and data products. Big data 5(3), 246\u2013255 (2017)","journal-title":"Big data"},{"key":"18_CR32","unstructured":"Vilone, G., Longo, L.: Explainable artificial intelligence: a systematic review (2020)"},{"key":"18_CR33","unstructured":"Wexler, R.: When a computer program keeps you in jail. NY Times 13 (2017)"},{"key":"18_CR34","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10618-008-0116-z","volume":"18","author":"C Whitrow","year":"2009","unstructured":"Whitrow, C., Hand, D.J., Juszczak, P., Weston, D., Adams, N.M.: Transaction aggregation as a strategy for credit card fraud detection. Data Min. Knowl. Disc. 18, 30\u201355 (2009)","journal-title":"Data Min. Knowl. Disc."},{"key":"18_CR35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/BF00992696","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 5\u201332 (1992). https:\/\/doi.org\/10.1007\/BF00992696","journal-title":"Mach. Learn."}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63800-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T23:16:29Z","timestamp":1720566989000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63800-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031637995","9783031638008"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63800-8_18","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valletta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/xaiworldconference.com\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}