Abstract
Insurance fraud is a non self-revealing type of fraud. The true historical labels (fraud or legitimate) are only as precise as the investigators’ efforts and successes to uncover them. Popular approaches of supervised and unsupervised learning fail to capture the ambiguous nature of uncertain labels. Imprecisely observed labels can be represented in the Dempster–Shafer theory of belief functions, a generalization of supervised and unsupervised learning suited to represent uncertainty. In this paper, we show that partial information from the historical investigations can add valuable, learnable information for the fraud detection system and improves its performances. We also show that belief function theory provides a flexible mathematical framework for concept drift detection and cost sensitive learning, two common challenges in fraud detection. Finally, we present an application to a real-world motor insurance claim fraud.
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References
Abdallah A, Maarof MA, Zainal A (2016) Fraud detection system: a survey. J Netw Comput Appl 68:90–113
Alippi C, Boracchi G, Roveri M (2013) Just-in-time classifiers for recurrent concepts. IEEE Trans Neural Netw Learn Syst 24(4):620–634
Anderson E (1935) The irises of the gaspe peninsula. Bull Am Iris Soc 59:2–5
Bahnsen AC, Aouada D, Ottersten B (2015) Example-dependent cost-sensitive decision trees. Expert Syst Appl 42(19):6609–6619
Barabesi L, Cerasa A, Cerioli A, Perrotta D (2018) Goodness-of-fit testing for the Newcomb–Benford law with application to the detection of customs fraud. J Bus Econ Stat 36(2):346–358
Bekker J, Davis J (2020) Learning from positive and unlabeled data: a survey. Mach Learn 109(4):719–760
Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 17(3):235–255
Brockett PL, Xia X, Derrig RA (1998) Using kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. J Risk Insurance, pp 245–274
Brown I, Mues C (2012) An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst Appl 39(3):3446–3453
Carcillo F, Le Borgne Y-A, Caelen O, Bontempi G (2018) Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization. Int J Data Sci Anal 5:285–300
Cerioli A, Barabesi L, Cerasa A, Menegatti M, Perrotta D (2019) Newcomb–Benford law and the detection of frauds in international trade. Proc Natl Acad Sci 116(1):106–115
Chapelle O, Scholkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge
Cherfi ZL, Oukhellou L, Côme E, Denoeux T, Aknin P (2012) Partially supervised independent factor analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis. Soft Comput 16(5):741–754
Coallition Against Insurance Fraud. https://insurancefraud.org/fraud-stats/. Accessed 5 May 2023
Côme E, Oukhellou L, Denœux T, Aknin P (2008) Mixture model estimation with soft labels. In: Soft methods for handling variability and imprecision. Springer, Berlin, pp 165–174
Cuzzolin F (2021) The geometry of uncertainty: the geometry of imprecise probabilities. Springer, Switzerland
Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2017) Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans Neural Netw Learn Syst 29(8):3784–3797
Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2015) Credit card fraud detection and concept-drift adaptation with delayed supervised information. In: 2015 International joint conference on neural networks (IJCNN). IEEE, pp 1–8
Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on machine learning, pp 233–240
Denoeux T (1995) A k-nearest neighbor classification rule based on Dempster–Shafer theory. IEEE Trans Syst Man Cybern 25(5):804–813
Derrig RA (2002) Insurance fraud. J Risk Insurance 69(3):271–287
Devroye L, Györfi L, Lugosi G (2013) A probabilistic theory of pattern recognition, vol 31. Springer, New York
Elkan C (2001) The foundations of cost-sensitive learning. In: International Joint Conference on Artificial Intelligence, vol 17, pp 973–978. Lawrence Erlbaum Associates Ltd
Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 213–220
European Union. https://europa.eu/youreurope/citizens/vehicles/insurance/accident/index_en.htm#shortcut-0. Accessed 26 July 2022
FBI Insurance Fraud. https://www.fbi.gov/stats-services/publications/insurance-fraud. Accessed 5 May 2023
Hand DJ, Anagnostopoulos C (2022) Notes on the h-measure of classifier performance. Adv Data Anal Classif, 1–16
Höppner S, Baesens B, Verbeke W, Verdonck T (2022) Instance-dependent cost-sensitive learning for detecting transfer fraud. Eur J Oper Res 297(1):291–300
Insurance Europe. https://www.insuranceeurope.eu/priorities/23/fraud-prevention. Accessed 5 May 2023
Liang C, Zhang Y, Shi P, Hu Z (2012) Learning very fast decision tree from uncertain data streams with positive and unlabeled samples. Inf Sci 213:50–67
Malekian D, Hashemi MR (2013) An adaptive profile based fraud detection framework for handling concept drift. In: 2013 10th International ISC conference on information security and cryptology (ISCISC), pp 1–6. IEEE
Nguyen Q, Valizadegan H, Hauskrecht M (2011) Learning classification with auxiliary probabilistic information. In: 2011 IEEE 11th international conference on data mining, pp 477–486. IEEE
Nian K, Zhang H, Tayal A, Coleman T, Li Y (2016) Auto insurance fraud detection using unsupervised spectral ranking for anomaly. J Finance Data Sci 2(1):58–75
O’Hagan A (2019) Expert knowledge elicitation: subjective but scientific. Am Stat 73(sup1):69–81
Quost B, Denoeux T, Li S (2017) Parametric classification with soft labels using the evidential em algorithm: linear discriminant analysis versus logistic regression. Adv Data Anal Classif 11(4):659–690
Ross GJ, Adams NM, Tasoulis DK, Hand DJ (2012) Exponentially weighted moving average charts for detecting concept drift. Pattern Recognit Lett 33(2):191–198
Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10(3):0118432
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton
Šimecková M (2005) Maximum weighted likelihood estimator in logistic regression. In: WDS, vol 5, pp 144–148
Smets P (1989) Constructing the pignistic probability function in a context of uncertainty. In: UAI, vol 89, pp 29–40
Sparrow MK (2008) Fraud in the us health-care system: exposing the vulnerabilities of automated payments systems. Soc Res: Int Q 75(4):1151–1180
Stripling E, Baesens B, Chizi B, vanden Broucke S (2018) Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers’ compensation fraud. Decis Support Syst 111:13–26
Šubelj L, Furlan Š, Bajec M (2011) An expert system for detecting automobile insurance fraud using social network analysis. Expert Syst Appl 38(1):1039–1052
The Association of British Insurers. https://www.abi.org.uk/products-and-issues/topics-and-issues/fraud/. Accessed 5 May 2023
Tsymbal A (2004) The problem of concept drift: definitions and related work. Comput Sci Dept Trinity Coll Dublin 106(2):58
Vanderschueren T, Verdonck T, Baesens B, Verbeke W (2022) Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies. Inf Sci 594:400–415
Viaene S, Dedene G (2004) Insurance fraud: issues and challenges. Geneva Pap Risk Insurance-Issues Pract 29(2):313–333
Webb GI, Hyde R, Cao H, Nguyen HL, Petitjean F (2016) Characterizing concept drift. Data Min Knowl Disc 30(4):964–994
Yager RR, Liu L (2008) Classic works of the Dempster–Shafer theory of belief functions, vol 219. Springer, Berlin
Yaghlane AB, Denœux T, Mellouli K (2008) Elicitation of expert opinions for constructing belief functions. In: Uncertainty and intelligent information systems. World Scientific, Singapore, pp 75–89
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Vandervorst, F., Verbeke, W. & Verdonck, T. Claims fraud detection with uncertain labels. Adv Data Anal Classif 18, 219–243 (2024). https://doi.org/10.1007/s11634-023-00568-0
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DOI: https://doi.org/10.1007/s11634-023-00568-0