Machine learning para la segmentación y optimización de los costos de adquisición de clientes

Este trabajo desarrolla un avance importante en la clasificación, predicción y segmentación de los afiliados a Casur, como parte de sus objetivos estratégicos facilitando la planeación de la entidad. Para ello, se adoptaron modelos básicos, de reglas, y posteriormente modelos machine learning de clasificación y clustering. De los cuales los primeros se utilizaron como base de comparación y los últimos dos se utilizaron como insumo en el resto del análisis. No obstante, debido a que los modelos tienen asociados error, se utilizó el modelo de distribución de probabilidad binomial para hallar el número de acercamientos comerciales necesarios para tener, como mínimo, una venta con una probabilidad del 99%; y optimizar, por esta vía, el modelo de adquisición de clientes, con lo que también se pudo comparar el mejor modelo, de acuerdo con la actualidad de la entidad.This work develops an important advance in the classification, prediction and segmentation of Casur affiliates, as part of its strategic objectives, facilitating the entity's planning. To do this, basic rule models were adopted, followed by machine learning classification and clustering models. Of which the first were used as a basis for comparison and the last two were used as input in the rest of the analysis. However, since the models have associated errors, the binomial probability distribution model was used to find the number of commercial approaches necessary to have, at least, one sale with a probability of 99%; and optimize, in this way, the customer acquisition model, with which it was also possible to compare the best model, according to the current state of the entity

Analysing the impact of post-pandemic factors on entrepreneurial intentions: The enduring significance of self-efficacy in student planned behaviour.

After the pandemic, there has still been an increased interest in examining university students’ entrepreneurial goals. In this study, we looked at the practicality and validity of using self-efficacy to broaden the theory of planned behavior (TPB) in assessing students’ intent to be entrepreneurs. Additionally, we looked at how students’ geographic location and gender affected their plans to start their businesses. Following the epidemic, we analyzed data obtained from a number of university students in both urban and rural regions of India using PLS-SEM and ANN methods. Our study confirmed the pivotal role that university students’ self-efficacy had in their entrepreneurial goals. The results of multi-group analysis (MGA) reported the insignificant moderating role of gender for the students’ entrepreneurial intentions. Still, they found a statistically significant difference in their said behavior control for entrepreneurial intentions regarding location. Based on their perceived behavioral control, the findings also suggest that youths in rural areas had lower entrepreneurial inclinations than urban students. The study indicated that considering the importance of student self-efficacy, universities should focus on improving students’ skill sets and problem-solving mindsets while constructing education courses

A neural network-based approach in predicting consumers' intentions of purchasing insurance policies

Insurance is a crucial mechanism used to lighten the financial burden as it provides protection against financial losses resulting from unexpected events. Insurers adopt various approaches, such as machine learning, to attract the uninsured. By using machine learning, a company is able to tap into the wealth of information of its potential customers. The main objective of this study is to apply artificial neural networks (ANNs) to predict the propensity of consumers to purchase an insurance policy by using the dataset from the Computational Intelligence and Learning (CoIL) Challenge 2000. In addition, this study also aims to identify factors that affect the propensity of customers to purchase insurance policies via feature selection. The dataset is pre-processed with feature construction and three feature selection methods, which are the neighbourhood component analysis (NCA), sequential forward selection (SFS) and sequential backward selection (SBS). Sampling techniques are carried out to address the issue of imbalanced class distributions. The results obtained are found to be comparable with the top few entries of the CoIL Challenge 2000, which shows the efficiency of the proposed model in predicting consumers’ intention of purchasing insurance policies

Educación financiera, comportamiento crediticio de riesgo y estilo de vida minimalista: Evidencia intergeneracional desde un enfoque SEM-ANN

El estilo de vida minimalista representa una oportunidad para enfocarse en aquello que proporciona mayor bienestar y crecimiento personal. A pesar de su creciente popularidad entre generaciones jóvenes e influencia sobre el proceso de toma de decisión, este estilo de vida ha recibido poca atención de la academia. En particular, la expresión del estilo de vida minimalista en el proceso de toma de decisión financiera en países en desarrollo representa un área de investigación inexplorada. Con el fin de abordar esta brecha en la investigación, se analizó el efecto moderador del estilo de vida minimalista sobre la relación entre la educación financiera y el comportamiento crediticio de riesgo, y se determinó si existen diferencias significativas entre individuos de la generación X e Y. A la luz de los resultados, se encontró que la educación financiera tiene un efecto negativo y significativo sobre el comportamiento crediticio de riesgo. Por otro lado, se observó que la adopción del estilo de vida minimalista fortalece el efecto negativo de la educación financiera sobre el comportamiento de riesgo relacionado al uso de crédito de consumo. Sin embargo, el efecto moderador solo fue significativo entre individuos de la generación Y

Impacto dos fatores de expectativa na continuidade de uso dos usuários de fintechs

Com a aceleração da digitalização, muitas empresas tem buscado na tecnologia um meio de aprimorar o desempenho de seus produtos, processos e gestão, entregando aos usuários uma experiência cada vez melhor. Desta forma entender fatores que levam os usuários a continuar utilizando tecnologias torna-se essencial para as empresas se destacarem em um cenário competitivo. No setor financeiro, esse movimento tem se consolidado através do crescimento das fintechs, com um número crescente de usuários que passou a adotá-las. Este trabalho tem como objetivo analisar os impactos dos fatores de expectativa: confiança, valor, riscos e qualidade do sistema na continuidade de uso dos usuários de fintechs. O objetivo desta pesquisa foi alcançado com a realização de 3 artigos. O artigo 1 tem como objetivo analisar as relações dos fatores de expectativa: confiança, valor, riscos e qualidade do sistema na continuidade de uso através de uma revisão de literatura e meta-análise, propondo um modelo de pesquisa. Foi realizada uma revisão de literatura, seguida de uma análise de peso e meta-análise em artigos selecionados das bases Scopus, Web of Science e Emerald para propor o modelo de pesquisa. Um total de 116 artigos foram selecionados para as análises. Foi identificado que o principal preditor da continuidade de uso é o valor, seguido de confiança e qualidade do sistema. O modelo de pesquisa proposto pode auxiliar estudos futuros que avaliem a continuidade de uso utilizando estes fatores de expectativa. O artigo 2 busca medir os impactos dos fatores de expectativa: confiança, valor, riscos e qualidade do sistema na continuidade de uso dos usuários de fintechs. Através de uma survey online com 426 respondentes, buscou-se medir os impactos dos fatores de expectativa confiança, valor, riscos e qualidade do sistema na continuidade de uso das fintechs. SPSS e SmartPLS 3.0 foram utilizados para executar técnicas estatísticas multivariadas para análise de dados. Os resultados demonstram que o valor é o principal fator que impacta a intenção de continuidade de uso destas empresas. Das sete hipóteses propostas, seis foram suportadas. O artigo 3 tem como objetivo estudar os impactos dos fatores de expectativa: confiança, valor, riscos e qualidade do sistema na continuidade de uso dos usuários de diferentes categorias de fintechs. SPSS e SmartPLS 3.0 foram utilizados para executar técnicas estatísticas multivariadas para análise de dados, especialmente MICOM e MGA. Os resultados demonstram que os impactos dos fatores variam conforme a categoria de fintech analisada. Especialmente as fintechs de serviços digitais e pagamentos, a qualidade do sistema e os riscos são os principais fatores relacionados a continuidade de uso, juntamente com o valor. Enquanto nas fintechs de investimentos, apenas o fator valor se relaciona com a continuidade de uso.With the acceleration of digitization, many companies have been looking to technology as a way to improve the performance of their products, processes, and management, providing users with an increasingly better experience. Thus, understanding factors that lead users to continue using technologies becomes essential for companies to stand out in a competitive scenario. In the financial sector, this movement has been consolidated through the growth of fintechs, with a growing number of users who started to adopt them. This work aims to analyze the impacts of expectation factors: trust, value, risks and system quality on the continued use of fintech users. The objective of this research was achieved with the completion of 3 articles. Article 1 aims to analyze the relationship of expectation factors: trust, value, risks and quality of the system in the continued use through a literature review and meta-analysis, proposing a research model. A literature review was performed, followed by a weight analysis and meta-analysis on selected articles from the Scopus, Web of Science and Emerald databases to propose the research model. A total of 116 articles were selected for analysis. It was identified that the main predictor of continued use is value, followed by system quality and quality. The proposed research model can help future studies that assess the continued use using these expectation factors. Article 2 seeks to measure the impacts of expectation factors: trust, value, risks and system quality on the continued use of fintech users. Through an online survey with 426 respondents, we sought to measure the impacts of the expected factors of trust, value, risks and system quality on the continued use of fintechs. SPSS and SmartPLS 3.0 were used to perform multivariate statistical techniques for data analysis. The results show that the value is the main factor that impacts the continuance intention using these companies. Of the seven proposed hypotheses, six were supported. Article 3 aims to study the impacts of expectation factors: trust, value, risks and system quality on the continued use of users of different categories of fintechs. SPSS and SmartPLS 3.0 were used to perform multivariate statistical techniques for data analysis, especially MICOM and MGA. The results demonstrate that the impacts of the factors vary according to the analyzed fintech category. Especially digital services and payments fintechs, system quality and risks are the main factors related to continued use, along with value. While in investment fintechs, only the value factor is related to the continued use