Financial fraud detection using naive bayes algorithm in highly imbalance data set

Gupta, Amit; Lohani, Manoj Chandra; Manchanda, Mahesh

doi:10.1080/09720529.2021.1969733

Cited by 32 publications

(8 citation statements)

References 17 publications

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“…NB did not obtain the best result when comparing with other classifiers. In Gupta, Lohani & Manchanda (2021) , among ML algorithms such as LR, RF, and SVM, the NB algorithm’s performance is remarkable. BBN applied in Kumar, Mubarak & Dhanush (2020) for detecting fraud in credit card.…”

Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.

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Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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show abstract

“…The first one employs supervised learning algorithms [ 11 ], which require labeling the dataset in order to identify patterns that distinguish fraudulent from non-fraudulent transactions. We identified Neural Networks [ 12 ], Fuzzy Logic [ 13 ], Particle Swarm Optimization [ 14 ], Regression Model [ 15 ], Genetic Algorithm [ 16 ], Naive Bayes [ 17 ], and Decision Trees [ 18 ] as a few frequently used supervised learning techniques. Ghosh and Reilly [ 19 ] suggested that K-Nearest Neighborhood (KNN) shows promising results on credit card fraud detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A soft voting ensemble learning approach for credit card fraud detection

Azim Mim,

Majadi,

Mazumder

2024

Heliyon

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“…Within the scope of this research, the classification framework has been constructed, employing algorithms of distinct methodological underpinnings. Specifically, the selected algorithms include Naive Bayes, esteemed for its probabilistic foundation, alongside the ensemble-based Random Forest, and the individually decisive Decision Tree, each chosen for their unique attributes and relevance to the study's objectives [23][24][25][26][27].…”

Section: Model Selectionmentioning

confidence: 99%

Enhancing wildfire risk assessment through weather modeling and machine learning: a case study in Orange County, USA

Kahono

2024

p.ISST

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Forest wildfire is a significant threat, causing extensive environmental damage and loss of life. Accurate and effective risk assessments are essential for better understanding and mitigating this risk. In this study, we propose a novel approach that combines weather modeling and machine learning for improved wildfire risk assessment. Our case study focuses on Orange County, USA, known for its high wildfire susceptibility. The methodology involves collecting historical weather and wildfire data in the region. We employ a machine learning model that integrates weather data with other factors, including vegetation and topography, to predict wildfire risk. Through machine learning techniques, we analyze the relationships among these variables and generate accurate risk predictions for Orange County. Results indicate a significant enhancement in wildfire risk assessment with this approach. The developed model provides valuable insights into the factors influencing wildfires, aiding authorities, and policymakers in implementing effective mitigation strategies. By leveraging weather modeling and machine learning, we can enhance our understanding and management of wildfire risks, safeguarding the environment and communities. This research highlights the potential of these technologies to improve wildfire mitigation efforts. The findings contribute to a proactive approach to addressing the serious threat of forest wildfires.

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Financial fraud detection using naive bayes algorithm in highly imbalance data set

Cited by 32 publications

References 17 publications

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

A soft voting ensemble learning approach for credit card fraud detection

Enhancing wildfire risk assessment through weather modeling and machine learning: a case study in Orange County, USA

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