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A Two-Step Feature Space Transforming Method to Improve Credit Scoring Performance

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1297))

Abstract

The increasing amount of credit offered by financial institutions has required intelligent and efficient methodologies of credit scoring. Therefore, the use of different machine learning solutions to that task has been growing during the past recent years. Such procedures have been used in order to identify customers who are reliable or unreliable, with the intention to counterbalance financial losses due to loans offered to wrong customer profiles. Notwithstanding, such an application of machine learning suffers with several limitations when put into practice, such as unbalanced datasets and, specially, the absence of sufficient information from the features that can be useful to discriminate reliable and unreliable loans. To overcome such drawbacks, we propose in this work a Two-Step Feature Space Transforming approach, which operates by evolving feature information in a twofold operation: (i) data enhancement; and (ii) data discretization. In the first step, additional meta-features are used in order to improve data discrimination. In the second step, the goal is to reduce the diversity of features. Experiments results performed in real-world datasets with different levels of unbalancing show that such a step can improve, in a consistent way, the performance of the best machine learning algorithm for such a task. With such results we aim to open new perspectives for novel efficient credit scoring systems.

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Notes

  1. 1.

    https://www.ecb.europa.eu.

  2. 2.

    ftp://ftp.ics.uci.edu/pub/machine-learning-databases/statlog/.

  3. 3.

    http://scikit-learn.org.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Salvatore Carta, Gianni Fenu, Anselmo Ferreira, Diego Reforgiato Recupero & Roberto Saia

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  1. Salvatore Carta
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  2. Gianni Fenu
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  3. Anselmo Ferreira
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  4. Diego Reforgiato Recupero
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  5. Roberto Saia
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Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. Polytechnic Institute of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Carta, S., Fenu, G., Ferreira, A., Reforgiato Recupero, D., Saia, R. (2020). A Two-Step Feature Space Transforming Method to Improve Credit Scoring Performance. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham. https://doi.org/10.1007/978-3-030-66196-0_7

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