Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy

Levantesi, Susanna; Zacchia, Giulia

doi:10.3390/jrfm14030120

Cited by 15 publications

(21 citation statements)

References 64 publications

(79 reference statements)

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“…Finally, in this study, we introduced a machine learning technique to define variable importance measurement. Random Forest (RF) is powerful tool that has recently been used to solve the problems of prediction and variable importance measurement [16,62,63]). For this study, we used decision trees [64][65][66]), more specifically, classification and regression trees [67].…”

Section: Methodsmentioning

confidence: 99%

Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland

et al. 2021

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Financial knowledge is the main element of financial literacy, which is important for the sustainable development of individuals and society. Sustainability is a complex concept that spans many fields, including financial knowledge for all ages. Financial knowledge requires significant scientific research showing its impact on individuals and the economy, including non-cash payments. Consumer payment knowledge and its association with consumer financial behavior have long been a matter of widespread interest by researchers, but no in-depth, empirically based scientific research has been completed for Poland. The objective of this study was to examine factors associated with cashless payment behavior with an emphasis on the role of consumer financial knowledge. A total of 1100 interviews were carried out with Polish nationals aged 15 and above. The collected data were analyzed with the use of statistical methods, including analysis of variances (ANOVA), in order to examine consumers’ financial knowledge by basic economic and non-economic factors. Additionally, a data-mining method known as Random Forests was implemented for finding the variable importance in correlations between consumer financial knowledge and preferred methods of payment. The results revealed the diversity of factors influencing consumer behavior. Among the consumers’ personal traits, financial knowledge was one of the most important determinants of their payment choices. The results have implications for the design of payment processes. The results can be used by central banks to determine the directions of financial inclusion, as well as for stakeholders in the payments market.

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Section: Methodsmentioning

confidence: 99%

Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland

et al. 2021

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“…The results obtained by the D1-NN are compared against some of the most important classifiers of the state of the art. The accuracy is computed as in Equation (3). The results are shown in Table 3.…”

Section: Performance and Comparative Analysismentioning

confidence: 99%

“…The key role financial businesses have in society today is indisputable. In this context, the support represented by the applications of machine learning techniques in topics related to financial risk assessment, among other topics of financial interest, is relevant and is an active area of research [2,3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

Camacho-Urriolagoitia

López-Yáñez

Villuendas-Rey

et al. 2021

Applied Sciences

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The presence of machine learning, data mining and related disciplines is increasingly evident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall.

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“…En la literatura ya se han realizado análisis con metodologías que permitan desarrollarlos mediante técnicas alternativas para el análisis el conocimiento financiero como, por ejemplo, el machine learning (Levantesi et al, 2021). En este estudio se usa el operador OWA como herramienta híbrida, el cual se caracteriza por agregar y comparar la información según con el significado de sus características, es decir, la importancia que tiene la información más que su propia medición.…”

Section: Análisis Híbridounclassified

Análisis de los niveles de conocimiento financiero usando el operador OWA: caso Boyacá, Colombia

2021

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El objetivo principal de esta investigación es analizar el nivel de conocimiento financiero de los habitantes del departamento de Boyacá. Para esto se realiza un análisis descriptivo y un análisis híbrido con el operador de agregación de la media ponderada ordenada – OWA, desde tres perspectivas: el hábito de ahorro, el uso de servicios financieros y la mejora de la educación financiera. La investigación considera tres conceptos fundamentales sobre conocimientos financieros básicos, conformados por el interés compuesto, la inflación y la diversificación del riesgo. Así mismo, se considera la metodología de la Superintendencia Financiera de Colombia, con el fin de usar conceptos propios del entorno que permitan la contextualización del participante frente a interrogantes del mercado financiero nacional. Para esto se determina la muestra de estudio de forma probabilística por provincias, que corresponde a un total de 1914 unidades y se presentan las cinco preguntas que permiten brindar una aproximación fiable sobre conocimientos básicos en finanzas. Los hallazgos muestran que los boyacenses presentan dificultad en comprender las capacidades de diversificar y diferenciar el riesgo, lo cual afecta el nivel de conciencia y de responsabilidad de sus decisiones financieras. Como conclusión se destaca que la debilidad en el conocimiento financiero restringe el desarrollo de hábitos financieros que permitan otorgar mayor empoderamiento a las personas, por lo cual se hace pertinente la ejecución de planes de acción que redunden en alianzas público-privadas, de acuerdo con la Estrategia Nacional de Educación Económica y Financiera de Colombia.

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Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy

Cited by 15 publications

References 64 publications

Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland

Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland

Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

Análisis de los niveles de conocimiento financiero usando el operador OWA: caso Boyacá, Colombia

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