Skip to main content

Auto-Insurance Fraud Detection Using Machine Learning Classification Models

  • Conference paper
  • First Online:
Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 695))

Included in the following conference series:

  • 492 Accesses


This work explored six machine learning algorithms: Extreme Gradient Boosting (XGBoost), Logistic Regression, Random Forest, Decision tree, Support Vector Machine (SVM), and Naïve Bayes to determine the best algorithm for detecting insurance fraud. The following were used to evaluate the six models: Confusion matrix, Accuracy, Precision, Recall, and F1-measure. The result showed that Random Forest outperformed the others in terms of accuracy. Extreme Gradient Boosting (Xgboost) had the highest precision and F1-measure scores, while the Decision Tree had the highest Recall score. Although two methods (Analysis of Variance (ANOVA) and Random Forest Classifier) were compared to determine the best feature selection, the significant features were selected using the Random Forest classifier because of the many benefits of using this method. The results of this study will be beneficial to insurance companies, stakeholders and policyholders.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Viaene S, Dedene G (2004) Insurance fraud: issues and challenges. Geneva Pap Risk Insur-Issues Pract 29(2):313–333

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Danquah M, Otoo DM, Baah-Nuakoh A (2018) Cost efficiency of insurance firms in Ghana. Manag Decis Econ 39(2):213–225

    Article  Google Scholar 

  4. Gomes C, Jin Z, Yang H (2021) Insurance fraud detection with unsupervised deep learning. J Risk Insur 88(3):591–624

    Article  Google Scholar 

  5. Brinkmann J (2005) Understanding insurance customer dishonesty: outline of a situational approach. J Bus Ethics 61(2):183–197

    Article  MathSciNet  Google Scholar 

  6. Ngai EW, Hu Y, Wong YH et al (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569

    Article  Google Scholar 

  7. Aslam F, Hunjra AI, Ftiti Z et al (2022) Insurance fraud detection: evidence from artificial intelligence and machine learning. Res Int Bus Finance 62:101744

    Article  Google Scholar 

  8. Nian K, Zhang H, Tayal A et al (2016) Auto insurance fraud detection using unsupervised spectral ranking for anomaly. J Finance Data Sci 2(1):58–75

    Article  Google Scholar 

  9. Kemp G (2010) Fighting public sector fraud in the 21st century. Comput Fraud Secur 2010(11):16–18

    Article  Google Scholar 

  10. Abdullah M (2021) The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh. J Asian Bus Econ Stud

    Google Scholar 

  11. Forough J, Momtazi S (2021) Ensemble of deep sequential models for credit card fraud detection. Appl Soft Comput 99:106883

    Article  Google Scholar 

  12. Zhang X, Han Y, Xu W et al (2021) Hoba: a novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Inform Sci 557:302–316

    Article  Google Scholar 

  13. Liu Y, Yang M, Wang Y et al (2022) Applying machine learning algorithms to predict default probability in the online credit market: evidence from China. Int Rev Financ Anal 79:101971

    Article  Google Scholar 

  14. Carmona P, Dwekat A, Mardawi Z (2022) No more black boxes! explaining the predictions of a machine learning xgboost classifier algorithm in business failure. Res Int Bus Financ 61:101649

    Article  Google Scholar 

  15. Amini S, Elmore R, Öztekin Ö et al (2021) Can machines learn capital structure dynamics? J Corp Financ 70:102073

    Article  Google Scholar 

  16. Singh A, Jain A (2021) Hybrid bio-inspired model for fraud detection with correlation based feature selection. J Discrete Math Sci Crypt 24(5):1365–1374

    Google Scholar 

  17. Adewumi AO, Akinyelu AA (2017) A survey of machine-learning and nature-inspired based credit card fraud detection techniques. Int J Syst Assur Eng Manag 8(2):937–953

    Article  Google Scholar 

  18. Carneiro N, Figueira G, Costa M (2017) A data mining based system for credit-card fraud detection in e-tail. Decis Support Syst 95:91–101

    Article  Google Scholar 

  19. Van Vlasselaer V, Bravo C, Caelen O et al (2015) Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decis Support Syst 75:38–48

    Article  Google Scholar 

  20. Van Vlasselaer V, Eliassi-Rad T, Akoglu L et al (2017) Gotcha! network-based fraud detection for social security fraud. Manag Sci 63(9):3090–3110

    Article  Google Scholar 

  21. Severino MK, Peng Y (2021) Machine learning algorithms for fraud prediction in property insurance: empirical evidence using real-world microdata. Mach Learn Appl 5:100074

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Toluwalope Owolabi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Owolabi, T., Shahra, E.Q., Basurra, S. (2024). Auto-Insurance Fraud Detection Using Machine Learning Classification Models. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 695. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3042-5

  • Online ISBN: 978-981-99-3043-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics