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An Adaptive and Dynamic Heterogeneous Ensemble Model for Credit Scoring

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Digital-for-Development: Enabling Transformation, Inclusion and Sustainability Through ICTs (IDIA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1774))

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Abstract

The determination of the financial credibility of a person for a loan is a challenging task as many variables are taken into consideration. Recently, there has been a surge in the application of machine learning approaches in the design of robust and effective credit scoring models as part of the human social development agenda under the assumption that the variables will remain stable for a long time. However, in real-life, the behavior of customers changes over time and the variables used to quantify the financial credibility of a person for a loan such as past performances on debt obligations, profiling, main household, income and demographics tend to drift and evolve over time. This paper considers credit scoring as an ephemeral scenario as variables tend to drift over time and proposes the application of data stream learning techniques in credit scoring since they are tailored for incremental learning. This makes the scoring model to be able to detect and adapt to changes in the customer behavior.

We propose the Adaptive and Dynamic Heterogeneous Ensemble (ADHE) approach that is capable of learning incrementally and adapting to drifting variables and consists of models derived from different learning algorithms to exploit diversity. The prediction performance of ADHE is evaluated using datasets that are publicly available and we compared the accuracy and computational cost of ADHE with existing state of the art models. Our proposed approach performs significantly well when compared to existing state of the art benchmark models on prediction accuracy according to the non-parametric test.

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Acknowledgement

All resources used mobilized by the author. This work was not funded by an individual or organization.

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Authors and Affiliations

  1. University of Johannesburg, Auckland Park, Johannesburg, South Africa

    Tinofirei Museba

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  1. Tinofirei Museba
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Correspondence to Tinofirei Museba .

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Editors and Affiliations

  1. University of Johannesburg, Johannesburg, South Africa

    Patrick Ndayizigamiye

  2. University of Johannesburg, Johannesburg, South Africa

    Hossana Twinomurinzi

  3. University of Mpumalanga, Mbombela, South Africa

    Billy Kalema

  4. University of Johannesburg, Johannesburg, South Africa

    Kelvin Bwalya

  5. University of Mpumalanga, Mbombela, South Africa

    Mncedisi Bembe

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Museba, T. (2023). An Adaptive and Dynamic Heterogeneous Ensemble Model for Credit Scoring. In: Ndayizigamiye, P., Twinomurinzi, H., Kalema, B., Bwalya, K., Bembe, M. (eds) Digital-for-Development: Enabling Transformation, Inclusion and Sustainability Through ICTs. IDIA 2022. Communications in Computer and Information Science, vol 1774. Springer, Cham. https://doi.org/10.1007/978-3-031-28472-4_19

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  • DOI: https://doi.org/10.1007/978-3-031-28472-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28471-7

  • Online ISBN: 978-3-031-28472-4

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