A1519
Title: Green AI in the finance industry: Experiments with feature engineering in hybrid machine learning models
Authors: Marcos Machado - University of Twente (Netherlands) [presenting]
Abstract: As research and practice on applications of artificial intelligence (AI) exponentially increase, the support for deployment grows at the same rate. While a large amount of data available enables sophisticated methods to perform feature engineering, reaching higher accuracy, it is imperative to emphasize the computational costs and the efficiency level in which these models operate. The processing time and accuracy of individual and hybrid machine learning (ML) models obtained when predicting customer loyalty in financial settings are contrasted. Frameworks that account for feature engineering and green AI philosophy aspects are used separately within the individual and hybrid proposed approaches. The individual models refer to commonly used regressor-based algorithms (e.g., decision trees, gradient boosting, and LightGBM) widely applied in business problems. The hybrid models use k-Means to cluster customers before implementing the individual regressor-based models. The findings indicate that using a lower number of features results in a slightly smaller accuracy than models incorporating features. Besides, the tradeoff is explicitly illustrated between the higher accuracy and computational time of the hybrid ML models against the lower accuracy and computational time of the individual models when assessing customers' loyalty levels. Thus, the results provide managers with information regarding the model to be deployed based on their firms' specifications.
