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Analysis of institutional authors

Kandi, KianehAuthorGarcia-Dopico, AntonioCorresponding Author

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April 12, 2025
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Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms

Publicated to: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 7 (1): 20- - 2025-02-21 7(1), DOI: 10.3390/make7010020

Authors:

Kandi, K; García-Dopico, A
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Affiliations

Univ Politecn Madrid, Ctr Invest Simulac Computac, Madrid 28660, Spain - Author
Univ Politecn Madrid, Dept Arquitectura & Tecnol Sistemas Informat DATSI, Escuelta Tecn Super Ingn Informat, Madrid 28660, Spain - Author

Abstract

This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampling Technique (SMOTE) to address any imbalance in class distribution. This paper conducts an extensive literature review, comparing various machine learning methods, and proposes an innovative framework that compares LSTM with XGBoost to improve fraud detection accuracy. LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. By meticulously carrying out preprocessing tasks, constructing competent training models, and implementing ensemble techniques, our proposed framework demonstrates unwavering performance in accurately identifying fraudulent transactions. The comparison of LSTM and XGBoost shows that LSTM is more effective for our imbalanced dataset. Compared with XGBOOST's 97% accuracy, LSTM's accuracy is 99%. The final result emphasizes how crucial it is to select the optimal algorithm based on particular criteria within financial concerns, which will ultimately result in more reliable and knowledgeable credit score decisions.
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Keywords

Extreme gradient boosting (xgboost)Imbalanced dataseLong short-term memory (lstm) networkRecurrent neural network (rnn)Synthetic minority oversampling technique (smote)

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal MACHINE LEARNING AND KNOWLEDGE EXTRACTION due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 51/368, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-25:

  • WoS: 5
  • Scopus: 5
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 72.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 68 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/92917/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 33
  • Downloads: 23
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (KANDI, KIANEH) and Last Author (GARCIA DOPICO, ANTONIO).

the author responsible for correspondence tasks has been GARCIA DOPICO, ANTONIO.

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