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Extreme Wavelet Fast Learning Machine for Evaluation of the Default Profile on Financial Transactions

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  • Paulo Vitor Campos Souza

    (Information Governance Secretaries, Federal Center for Technological Education of Minas Gerais - CEFET-MG)

  • Luiz Carlos Bambirra Torres

    (Federal University of Ouro Preto)

Abstract

Extreme learning machines enable multilayered neural networks to perform activities to facilitate the process and business dynamics. It acts in pattern classification, linear regression problems, and time series prediction. The financial area needs efficient models that can perform businesses in a short time. Credit card fraud and debits occur regularly, and effective decision making can avoid significant obstacles for both clients and financial companies. This paper proposes a training model for multilayer networks where the weights of the training algorithm are defined by the nature and characteristics of the dataset using the concepts of the wavelet transform. The traditional algorithm of weights’ definition of the output layer is changed to a regularized method that acts more quickly in the description of the weights of the output layer. Finally, several activation functions are applied to the model to verify its efficiency in several scenarios. This model was subjected to an extensive dataset and comparing to different machine learning approaches. Its answers were satisfactory in a short-time execution, proving that the Extreme Learning Machine works efficiently to identify possible profiles of defaulters in payments in the financial relationships involving a credit card.

Suggested Citation

  • Paulo Vitor Campos Souza & Luiz Carlos Bambirra Torres, 2021. "Extreme Wavelet Fast Learning Machine for Evaluation of the Default Profile on Financial Transactions," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1263-1285, April.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:4:d:10.1007_s10614-020-10018-0
    DOI: 10.1007/s10614-020-10018-0
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    References listed on IDEAS

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    1. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    2. Koutanaei, Fatemeh Nemati & Sajedi, Hedieh & Khanbabaei, Mohammad, 2015. "A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 11-23.
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