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Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks

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  • Kwanda Sydwell Ngwenduna

    (School of Computer Science and Applied Mathematics, University of the Witwatersrand, West Campus, Mathematical Sciences Building, Private Bag 3, Wits, Braamfontein 2050, South Africa
    DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP), Wits, Braamfontein 2050, South Africa)

  • Rendani Mbuvha

    (School of Statistics and Actuarial Science, University of the Witwatersrand, West Campus, Mathematical Sciences Building, Private Bag 3, Wits, Braamfontein 2050, South Africa)

Abstract

To build adequate predictive models, a substantial amount of data is desirable. However, when expanding to new or unexplored territories, this required level of information is rarely always available. To build such models, actuaries often have to: procure data from local providers, use limited unsuitable industry and public research, or rely on extrapolations from other better-known markets. Another common pathology when applying machine learning techniques in actuarial domains is the prevalence of imbalanced classes where risk events of interest, such as mortality and fraud, are under-represented in data. In this work, we show how an implicit model using the Generative Adversarial Network (GAN) can alleviate these problems through the generation of adequate quality data from very limited or highly imbalanced samples. We provide an introduction to GANs and how they are used to synthesize data that accurately enhance the data resolution of very infrequent events and improve model robustness. Overall, we show a significant superiority of GANs for boosting predictive models when compared to competing approaches on benchmark data sets. This work offers numerous of contributions to actuaries with applications to inter alia new sample creation, data augmentation, boosting predictive models, anomaly detection, and missing data imputation.

Suggested Citation

  • Kwanda Sydwell Ngwenduna & Rendani Mbuvha, 2021. "Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks," Risks, MDPI, vol. 9(3), pages 1-33, March.
  • Handle: RePEc:gam:jrisks:v:9:y:2021:i:3:p:49-:d:513033
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. McCullagh, Peter, 1984. "Generalized linear models," European Journal of Operational Research, Elsevier, vol. 16(3), pages 285-292, June.
    3. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
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    Cited by:

    1. Szymon Kubiak & Tillman Weyde & Oleksandr Galkin & Dan Philps & Ram Gopal, 2023. "Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe," Papers 2311.16004, arXiv.org.
    2. Ricardo Mu~noz-Cancino & Cristi'an Bravo & Sebasti'an A. R'ios & Manuel Gra~na, 2022. "Assessment of creditworthiness models privacy-preserving training with synthetic data," Papers 2301.01212, arXiv.org.
    3. Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.

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