Financial Habits of Mexican Women using Machine Learning Algorithms

Abstract

This research was conducted under the Master in Computational Science program at Tecnológico de Monterrey. The proposal is a model to assess a profile risk for Mexican women, who require the service of a financial portfolio offered by a financial institution. Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. Therefore, there is a great interest for developing methods that correctly model the behavior, and aid the decision-making process in financial services. Several models in the State-of-art for this type of analysis is done with linear programming and statistical techniques. Therefore, this study will use a benchmark of Machine Learning algorithms, such as Unsupervised and Supervised Learning algorithms, to extract information on four different datasets relevant to the population of interest. The first phase involves applying state-of-the-art techniques on public datasets of the Mexican population, whereas the second phase involves a future research involving a financial institution to create the model for the Women segment. It was found that financial habits of the population are heavily dependent on the region. There also an important group in the population characterized for not possessing an account in a financial institution and also not having emergency funds. In the case of the profiles of women, the most important attributes were their civil status and their participation in the workforce. The largest group of women are housewives, though the second largest group consists of married women who also participate in the workforce.