Abstract
Healthcare insurance is intended to help pay for the insured’s medical expenses by paying a policy premium. For this, the industry needs the collaboration of some entities, such as: doctors, health care centers, brokers, insurers, reinsurers. In this context, gathering the information necessary to assess and process claims is a major problem. As a consequence, these inconveniences are exploited by fraudsters and scammers. Faced with these challenges, blockchain can help solve them. This paper defines blockchain and investigates how its inherent characteristics can contribute to detecting healthcare insurance fraud. Then, a layered overview and model using smart contracts are defined. Finally, conclusions and recommendations are issued to address its implementation in the insurance market.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Coalition Against Insurance Fraud: By the numbers fraud statistics, https://www.insurancefraud.org/statistics.htm. Accessed 12 Aug 2019
A.T. Mailloux, S.W. Cummings, M. Mugdh, A decision support tool for identifying abuse of controlled substances by ForwardHealth medicaid members. J. Hosp. Mark. Public Relations. 34–55 (2010). https://doi.org/10.1080/15390940903450982
J.C. Mendoza-Tello, H. Mora, F. Pujol, M.D. Lytras, Disruptive innovation of cryptocurrencies in consumer acceptance and trust. Inf. Syst. E-bus. Manag. 195–222 (2019). https://doi.org/10.1007/s10257-019-00415-w
A. Marotta, F. Martinelli, S. Nanni, A. Orlando, A. Yautsiukhin, Cyber-insurance survey. Comput. Sci. Rev. 24, 35–61 (2017). https://doi.org/10.1016/j.cosrev.2017.01.001
A. Abdallah, M.A. Maarof, A. Zainal, Fraud detection system: A survey. J. Netw. Comput. Appl. 68, 90–113 (2016). https://doi.org/10.1016/j.jnca.2016.04.007
D. Thornton, M. Brinkhuis, C. Amrit, R. Aly, Categorizing and describing the types of fraud in healthcare. Procedia Comput. Sci. 64, 713–720 (2015). https://doi.org/10.1016/j.procs.2015.08.594
R.M. Musal, Two models to investigate Medicare fraud within unsupervised databases. Expert Syst. Appl. 37, 8628–8633 (2010). https://doi.org/10.1016/j.eswa.2010.06.095
P.A. Ortega, G.A. Ruz, A medical claim fraud/abuse detection system based on data mining: A case study in Chile. in Proceedings of the 2006 International Conference on Data Mining, DMIN 2006. (Las Vegas, Nevada, USA, 2006). pp. 224–231
Konijn, R.M., Kowalczyk, W.: Finding Fraud in Health Insurance Data with Two-Layer Outlier Detection Approach. in Data Warehousing and Knowledge Discovery. DaWaK 2011. Lecture Notes in Computer Science, ed. by A. Cuzzocrea, U. Dayal (Springer Berlin Heidelberg, 2011). pp. 394–405 https://doi.org/10.1007/978-3-642-23544-3_30
Y. Li, C. Yan, W. Liu, M. Li, A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification. Appl. Soft Comput. 70, 1000–1009 (2018). https://doi.org/10.1016/j.asoc.2017.07.027
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mendoza-Tello, J.C., Mendoza-Tello, T., Mora, H. (2021). Blockchain as a Healthcare Insurance Fraud Detection Tool. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-62066-0_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62065-3
Online ISBN: 978-3-030-62066-0
eBook Packages: EducationEducation (R0)