TY - GEN
T1 - Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data
AU - Flores-Siguenza, Pablo
AU - Espinoza-Saquicela, Darío
AU - Moscoso-Martínez, Marlon
AU - Siguenza-Guzman, Lorena
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Data analysis and processing allow for acquiring competitive advantages both in the business and academic and research worlds. One of the sciences that carries out this analysis is machine learning, which has evolved with greater emphasis in recent years due to its advantages and applicability in different areas. Aware of the importance and current relevance of data management for industries, especially in the banking sector, this study applies supervised learning techniques to generate classification and prediction models by treating a set of data from an Ecuadorian financial institution. Different algorithms are compared, and each of the steps to follow in constructing the models is explained in detail. This allows the financial entity to classify its clients as VIPs or not with greater certainty, as well as to predict the investment amounts of the potential clients based on variables such as age, occupation, and among others. The main results show that the K-nearest neighbor algorithm with k = 5 is optimal for classification, while for prediction, the multilayer perceptron algorithm is the most favorable.
AB - Data analysis and processing allow for acquiring competitive advantages both in the business and academic and research worlds. One of the sciences that carries out this analysis is machine learning, which has evolved with greater emphasis in recent years due to its advantages and applicability in different areas. Aware of the importance and current relevance of data management for industries, especially in the banking sector, this study applies supervised learning techniques to generate classification and prediction models by treating a set of data from an Ecuadorian financial institution. Different algorithms are compared, and each of the steps to follow in constructing the models is explained in detail. This allows the financial entity to classify its clients as VIPs or not with greater certainty, as well as to predict the investment amounts of the potential clients based on variables such as age, occupation, and among others. The main results show that the K-nearest neighbor algorithm with k = 5 is optimal for classification, while for prediction, the multilayer perceptron algorithm is the most favorable.
KW - Classification model
KW - Data analysis
KW - Financial industry
KW - Machine learning
KW - Prediction model
UR - https://www.scopus.com/pages/publications/85174680864
U2 - 10.1007/978-981-99-3091-3_69
DO - 10.1007/978-981-99-3091-3_69
M3 - Contribución a la conferencia
AN - SCOPUS:85174680864
SN - 9789819930906
T3 - Lecture Notes in Networks and Systems
SP - 843
EP - 853
BT - Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
A2 - Yang, Xin-She
A2 - Sherratt, R. Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Congress on Information and Communication Technology, ICICT 2023
Y2 - 20 February 2023 through 23 February 2023
ER -