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

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.

Automated summary

Extreme Learning Machines enable multilayer neural networks to improve efficiency, particularly crucial in the financial sector for identifying defaulters in credit card financial relationships.