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Research Hotspots, Emerging Trend and Front of Fraud Detection Research: A Scientometric Analysis (1984–2021)

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Data Mining and Big Data (DMBD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1745))

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Abstract

This paper conducted a comprehensive scientometric review of Fraud Detection between 1984 and 2021 to depict the landscapes, research hotspots, and emerging trends in this field. Besides scientific outputs evaluation using statistical analysis and comparative analysis, scientometric methods such as co-occurrence analysis, cocitation analysis, and coupling analysis were used to analyze the knowledge structure of Fraud detection. Results showed that Fraud Detection research went up significantly in the past two decades, in addition to conventional scientometric results, keywords with the strongest citation burst such as Intrusion Detection, Audit Planning, Pattern Recognition, Data Mining, Insurance Fraud, Benfords Law, Business Intelligence, Outlier Detection, Genetic Algorithm, Big Data, and Deep Learning, demonstrate the emerging trends of Fraud Detection.

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References

  1. Fraud: Detection, Prevention, and Analytics! In: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques, pp. 1–36. John Wiley and Sons, Inc., Hoboken (2015)

    Google Scholar 

  2. Sun, C., Li, Q., Cui, L., Yan, Z., Li, H., Wei, W.: An effective hybrid fraud detection method. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS (LNAI), vol. 9403, pp. 563–574. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25159-2_51

    Chapter  Google Scholar 

  3. Yu, F., Yu, X.: Corporate lobbying and fraud detection. J. Financ. Quant. Anal. 46, 1865–1891 (2011)

    Article  Google Scholar 

  4. Viaene, S., Derrig, R.A., Baesens, B., Dedene, G.: A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. J. Risk Insur. 69, 373–421 (2002). https://doi.org/10.1111/1539-6975.00023

    Article  Google Scholar 

  5. Cecchini, M., Aytug, H., Koehler, G.J., Pathak, P.: Detecting management fraud in public companies. Manage. Sci. 56, 1146–1160 (2010). https://doi.org/10.1287/mnsc.1100.1174

    Article  MATH  Google Scholar 

  6. Bauder, R., Khoshgoftaar, T.M., Seliya, N.: A survey on the state of healthcare upcoding fraud analysis and detection. Health Serv. Outcomes Res. Method. 17(1), 31–55 (2016). https://doi.org/10.1007/s10742-016-0154-8

    Article  Google Scholar 

  7. Bolton, R., Hand, D.: Statistical fraud detection: a review. Stat. Sci. 17, 235–249 (2002)

    Article  MATH  Google Scholar 

  8. Kou, Y., et al.: Survey of fraud detection techniques. In: Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 749–754 (2004)

    Google Scholar 

  9. West, J., Bhattacharya, M.: Intelligent financial fraud detection: a comprehensive review. Comput. Secur. 57, 47–66 (2016)

    Article  Google Scholar 

  10. Ashtiani, M.N., Raahemi, B.: Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review. IEEE Access 10, 72504–72525 (2022)

    Article  Google Scholar 

  11. Callao, M.P., Ruisanchez, I.: An overview of multivariate qualitative methods for food fraud detection. Food Control 86, 283–293 (2018)

    Article  Google Scholar 

  12. Omair, B., Alturki, A.: A systematic literature review of fraud detection metrics in business processes. IEEE Access 8, 26893–26903 (2020)

    Article  Google Scholar 

  13. Pourhabibi, T., et al.: Fraud detection: a systematic literature review of graph-based anomaly detection approaches. Decis. Support Syst. 133, 113303 (2020)

    Article  Google Scholar 

  14. Gupta, S., Mehta, S.K.: Data mining-based financial statement fraud detection: systematic literature review and meta-analysis to estimate data sample mapping of fraudulent companies against non-fraudulent companies. Glob. Bus. Rev. (2021). 097215092098485

    Google Scholar 

  15. Al-Hashedi, K.G., Magalingam, P.: Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019. Comput. Sci. Rev. 40, 100402 (2021)

    Article  Google Scholar 

  16. Nana, W., Wang, F.: Comparative analysis on domestic and foreign financial fraud research based on knowledge map-web of science core collection. In: Proceedings of the 2017 4th International Conference on Management Innovation and Business Innovation (ICMIBI 2017), vol. 81, no. 2017, pp. 81–90 (2017)

    Google Scholar 

  17. Mansour, A.Z., Ahmi, A., Popoola, O.M.J., Znaimat, A.: Discovering the global landscape of fraud detection studies: a bibliometric review. J. Financ. Crime 29, 701–720 (2021). https://doi.org/10.1108/jfc-03-2021-0052

    Article  Google Scholar 

  18. van Eck, N.J., Waltman, L.: VOS: a new method for visualizing similarities between objects. In: Decker, R., Lenz, H.-J. (eds.) Advances in Data Analysis. SCDAKO, pp. 299–306. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70981-7_34

    Chapter  Google Scholar 

  19. Li, Z., Li, Z., Zhao, Z., et al.: Landscapes and emerging trends of virtual reality in recent 30 years: a bibliometric analysis. In: Proceedings of the 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE (2018)

    Google Scholar 

  20. Aria, M., Cuccurullo, C.: Bibliometrix : an R-tool for comprehensive science mapping analysis. J. Informet. 11(4), 959–975 (2017)

    Article  Google Scholar 

  21. Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inform. Sci. Technol. 57(3), 359–377 (2006)

    Article  Google Scholar 

  22. Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2002. ACM Press (2002)

    Google Scholar 

  23. Rogosa, D., Brandt, D., Zimowski, M.: A growth curve approach to the measurement of change. Psychol. Bull. 92(3), 726–748 (1982)

    Article  Google Scholar 

  24. Egghe, L.: Theory and practise of the g-index. Scientometrics 69, 131–152 (2006)

    Article  Google Scholar 

  25. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  MATH  Google Scholar 

  26. Chen, C., Leydesdorff, L.: Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. J. Am. Soc. Inf. Sci. 65(2), 334–351 (2013)

    Google Scholar 

  27. Fawcett, T., Provost, F.: Adaptive fraud detection. Data Min. Knowl. Discovery 1, 291–316 (1997). https://doi.org/10.1023/A:1009700419189

    Article  Google Scholar 

  28. Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50, 559–569 (2011). https://doi.org/10.1016/j.dss.2010.08.006

    Article  Google Scholar 

  29. Chan, P., Fan, W., Prodromidis, A., Stolfo, S.: Distributed data mining in credit card fraud detection. IEEE Intell. Syst. Their Appl. 14, 67–74 (1999). https://doi.org/10.1109/5254.809570

    Article  Google Scholar 

  30. Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016). https://doi.org/10.1016/j.jnca.2016.04.007

    Article  Google Scholar 

  31. Srivastava, A., Kundu, A., Sural, S., Majumdar, A.K.: Credit card fraud detection using hidden Markov model. IEEE Trans. Dependable Secure Comput. 5, 37–48 (2008). https://doi.org/10.1109/TDSC.2007.70228

    Article  Google Scholar 

  32. Sahin, Y., Bulkan, S., Duman, E.: A cost-sensitive decision tree approach for fraud detection. Expert Syst. Appl. 40, 5916–5923 (2013). https://doi.org/10.1016/j.eswa.2013.05.021

    Article  Google Scholar 

  33. Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., Bontempi, G.: Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl. 41, 4915–4928 (2014). https://doi.org/10.1016/j.eswa.2014.02.026

    Article  Google Scholar 

  34. Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inform. Sci. 24, 265–269 (1973). https://doi.org/10.1002/asi.4630240406

    Article  Google Scholar 

  35. Correa Bahnsen, A., Aouada, D., Stojanovic, A., Ottersten, B.: Feature engineering strategies for credit card fraud detection. Expert Syst. Appl. 51, 134–142 (2016). https://doi.org/10.1016/j.eswa.2015.12.030

    Article  Google Scholar 

  36. Van Vlasselaer, V., et al.: APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015). https://doi.org/10.1016/j.dss.2015.04.013

    Article  Google Scholar 

  37. Jurgovsky, J., et al.: Sequence classification for credit-card fraud detection. Expert Syst. Appl. 100, 234–245 (2018). https://doi.org/10.1016/j.eswa.2018.01.037

    Article  Google Scholar 

  38. Nami, S., Shajari, M.: Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors. Expert Syst. Appl. 110, 381–392 (2018). https://doi.org/10.1016/j.eswa.2018.06.011

    Article  Google Scholar 

  39. Rushin, G., Stancil, C., Sun, M., Adams, S., Beling, P.: Horse race analysis in credit card fraud—deep learning, logistic regression, and gradient boosted tree. In: Proceedings of the 2017 Systems and Information Engineering Design Symposium (SIEDS). IEEE (2017)

    Google Scholar 

  40. Zheng, L., Liu, G., Yan, C., Jiang, C.: Transaction fraud detection based on total order relation and behavior diversity. IEEE Trans. Comput. Soc. Syst. 5, 796–806 (2018). https://doi.org/10.1109/tcss.2018.2856910

    Article  Google Scholar 

  41. Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016)

    Google Scholar 

  42. Herland, M., Khoshgoftaar, T.M., Bauder, R.A.: Big Data fraud detection using multiple medicare data sources. J. Big Data 5(1), 1–21 (2018). https://doi.org/10.1186/s40537-018-0138-3

    Article  Google Scholar 

  43. Wang, Y., Xu, W.: Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis. Support Syst. 105, 87–95 (2018)

    Article  Google Scholar 

  44. Hodge, A.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85–126 (2004). https://doi.org/10.1007/s10462-004-4304-y

    Article  MATH  Google Scholar 

  45. Maranzato, R., Pereira, A., Neubert, M., do Lago, A.P.: Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization. ACM SIGAPP Appl. Comput. Rev. 11, 14–26 (2010)

    Google Scholar 

  46. Cleary, R., Thibodeau, J.C.: Applying digital analysis using Benford’s law to detect fraud: the dangers of type I errors. Auditing J. Pract. Theory 24, 77–81 (2005). https://doi.org/10.2308/aud.2005.24.1.77

    Article  Google Scholar 

  47. Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., Song, L.: Heterogeneous graph neural networks for malicious account detection. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, New York (2018)

    Google Scholar 

  48. Wang, D., et al.: A semi-supervised graph attentive network for financial fraud detection. In: Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM). IEEE (2019)

    Google Scholar 

  49. Liu, Z., Dou, Y., Yu, P.S., Deng, Y., Peng, H.: Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of International Business and Management, Sichuan International Studies University, Chongqing, 400000, China

    Li Zeng

  2. Hunan Equastar Technology Co. Ltd., Hunan, 410000, China

    Yang Li

  3. Hunan Institute of Traffic Engineering, Hunan, 421009, China

    Zili Li

Authors
  1. Li Zeng
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Correspondence to Yang Li .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Zeng, L., Li, Y., Li, Z. (2022). Research Hotspots, Emerging Trend and Front of Fraud Detection Research: A Scientometric Analysis (1984–2021). In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_8

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  • DOI: https://doi.org/10.1007/978-981-19-8991-9_8

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  • Online ISBN: 978-981-19-8991-9

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