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Naïve Bayes Based Classifier for Credit Card Fraud Discovery

  • Conference paper
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Information Systems (EMCIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 437))

Abstract

As financial services and operations expand, financial fraud is on the rise. Despite the use of preventative and security measures to reduce monetary fraud, criminals are constantly acquiring and developing new ways to circumvent fraud detection systems, posing a challenge to quantitative methods and predictive approaches. As a result, new methodologies must be researched and tested to leverage the insights gained from the study to assist further incorrect fraud forecasting and the establishment of fraud discovery schemes with extra measures to alleviate distrustful events. Naïve Bayes (NB) is a significant Machine Learning (ML) classifier that is not yet explored in the literature, unlike the use of common ML techniques like Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and the likes. This paper, therefore, explores the use of a technique yet to be employed for credit card fraud detection (CCFD) namely Naïve Bayes. The classifier was compared using a confusion matrix for performance matrices like accuracy, precision, recall, f-measure, and ROC-AUC. It was discovered that NB outperformed most of the ML classifiers employed in state-of-the-art compared with an accuracy of 97.99%, recall of 98.02%, the precision of 99.97%, f-measure 98.98%, and FPR of 0.1971.

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Abbreviations

Abbreviation :

Meaning

ML:

Machine Learning

DT:

Decision Tree

KNN:

K-Nearest Neighbor

CC:

Credit Card

CCF:

Credit Card Fraud

FPR:

False Positive Rate

TP:

True Positive

TN:

True Negative

FP:

False Positive

FN:

False Negative

FL:

Fuzzy Logic

LR:

Logistic Regression

DM:

Data Mining

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Author information

Authors and Affiliations

  1. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Roseline Oluwaseun Ogundokun & Jide Kehinde Adeniyi

  2. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

  3. Directorate of Financial Services, Agricultural and Rural Management Training Institute, Ilorin, Nigeria

    Olufunmilayo Joyce Fatigun

Authors
  1. Roseline Oluwaseun Ogundokun
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  2. Sanjay Misra
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  3. Olufunmilayo Joyce Fatigun
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  4. Jide Kehinde Adeniyi
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Corresponding author

Correspondence to Roseline Oluwaseun Ogundokun .

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Editors and Affiliations

  1. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

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Ogundokun, R.O., Misra, S., Fatigun, O.J., Adeniyi, J.K. (2022). Naïve Bayes Based Classifier for Credit Card Fraud Discovery. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_37

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  • DOI: https://doi.org/10.1007/978-3-030-95947-0_37

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