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

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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.

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Notes

  1. Information of \(\sigma =\) scale and \(\varUpsilon =\) translation factor.

  2. According to the experiments performed in the original paper, preliminary approaches to defining the parameter were compared. Therefore the value was chosen after tests with values ranging from \(2^{-25}\) to \(2^{25}\). Further information can be viewed at Huang et al. (2012).

  3. All parameters used in the models were set as the defaults provided by the Weka tool.

  4. For a group of values of 50, 100, 150, 200, 250, 300.

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Authors and Affiliations

  1. Information Governance Secretaries, Federal Center for Technological Education of Minas Gerais - CEFET-MG, Av. Amazonas, 5.253, 30.421-169, Belo Horizonte, MG, Brazil

    Paulo Vitor de Campos Souza

  2. Department of Computing and Systems, Federal University of Ouro Preto, 36, 115th Street - 35.931-008, Loanda, João Monlevade, MG, Brazil

    Luiz Carlos Bambirra Torres

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de Campos Souza, P.V., Torres, L.C.B. Extreme Wavelet Fast Learning Machine for Evaluation of the Default Profile on Financial Transactions. Comput Econ 57, 1263–1285 (2021). https://doi.org/10.1007/s10614-020-10018-0

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