Extreme wavelet fast learning machine for evaluation of the default profle on financial transactions.

Abstract

Extreme learning machines enable multilayered neural networks to perform activities to facilitate the process and business dynamics. It acts in pattern classifcation, linear regression problems, and time series prediction. The fnancial area needs efcient models that can perform businesses in a short time. Credit card fraud and debits occur regularly, and efective decision making can avoid signifcant obstacles for both clients and fnancial companies. This paper proposes a training model for multilayer networks where the weights of the training algorithm are defned by the nature and characteristics of the dataset using the concepts of the wavelet transform. The traditional algorithm of weights’ defnition 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 efciency in several scenarios. This model was subjected to an extensive dataset and comparing to diferent machine learning approaches. Its answers were satisfactory in a short-time execution, proving that the Extreme Learning Machine works effciently to identify possible profles of defaulters in payments in the fnancial relationships involving a credit card.