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    • Propensity Model using decision trees (LightGBM) for the Management of the Effective Credit Product in a Financial Entity.

    Original Research (Published On: 09-Jan-2025 )
    DOI : https://doi.org/10.54364/AAIML.2025.51184

    Norberto Ulises Roman Concha

    Adv. Artif. Intell. Mach. Learn., 5 (1):3202-3215

    1. Norberto Ulises Roman Concha: National University of San Marcos

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    DOI: 10.54364/AAIML.2025.51184

    Article History: Received on: 16-Sep-24, Accepted on: 03-Jan-25, Published on: 09-Jan-25

    Corresponding Author: Norberto Ulises Roman Concha

    Email: nromanc@unmsm.edu.pe

    Citation: Ulises Roman-Concha, et al. Propensity Model Using Decision Trees (LightGBM) for the Management of the Effective Credit Product in a Financial Entity. Advances in Artificial Intelligence and Machine Learning. 2025;5(1):184.


    Abstract

        

    The objective of this paper was to develop a propensity model based on decision trees (LightGbm) for the management of the Credit product in a financial institution. The CRIPS-DM methodology was used as a framework and Python/LightGBM was used for the development of the solution. As a result, it was possible to increase by 5% the effectiveness in credit for each month on a park of 200 thousand customers of the financial institution, which ensures the understanding of the applied model.

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