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Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data

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Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 694))

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Abstract

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.

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Acknowledgements

The authors would like to thank to “Vicerrectorado de Investigación” of the University of Cuenca, Ecuador, for the financial support given to the present research, development, and innovation work.

Author information

Authors and Affiliations

  1. Department of Applied Chemistry and Systems of Production, Faculty of Chemical Sciences, Universidad de Cuenca, Cuenca, Ecuador

    Pablo Flores-Siguenza

  2. Institute of Sectional Regime Studies of Ecuador, Universidad del Azuay, Cuenca, Ecuador

    Darío Espinoza-Saquicela

  3. Faculty of Sciences, Escuela Superior Politécnica de Chimborazo (ESPOCH), Panamericana Sur Km 1 1/2, 060106, Riobamba, Ecuador

    Marlon Moscoso-Martínez

  4. Department of Computer Sciences, Faculty of Engineering, Universidad de Cuenca, Cuenca, Ecuador

    Lorena Siguenza-Guzman

  5. Research Centre Accountancy, Faculty of Economics and Business, KU Leuven, Leuven, Belgium

    Lorena Siguenza-Guzman

Authors
  1. Pablo Flores-Siguenza
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  2. Darío Espinoza-Saquicela
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  3. Marlon Moscoso-Martínez
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  4. Lorena Siguenza-Guzman
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Corresponding author

Correspondence to Lorena Siguenza-Guzman .

Editor information

Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University London, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, University of Reading, England, UK

    R. Simon Sherratt

  3. Department of Computer Science and Engineering, Techno International Newtown, Chakpachuria, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Flores-Siguenza, P., Espinoza-Saquicela, D., Moscoso-Martínez, M., Siguenza-Guzman, L. (2023). Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-99-3091-3_69

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  • DOI: https://doi.org/10.1007/978-981-99-3091-3_69

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3090-6

  • Online ISBN: 978-981-99-3091-3

  • eBook Packages: EngineeringEngineering (R0)

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