Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

Wu, Banghua; Lv, Xuebin; Al-Ghamdi, Abdullah Ahmed; Abosaq, Hamad; Alrizq, Mesfer

doi:10.1016/j.ipm.2022.103231

Cited by 7 publications

(1 citation statement)

References 47 publications

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“…Another challenge is the interpretability of machine learning algorithms. The complexity and black-box nature of some machine learning algorithms can make it difficult for financial institutions to interpret and explain the decisions made by these algorithms (Wu et al, 2023). Moreover, the lack of necessary skillsets and expertise within financial institutions can pose a challenge when implementing machine learning algorithms for risk management and fraud detection.…”

Section: Challenges Associated With Implementing Machine Learning In ...mentioning

confidence: 99%

Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

Kumar,

Singh

2024

Int. J. Res. Publ. Rev

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This study examines the impact of machine learning algorithms on risk management and fraud detection in financial institutions. Machine learning has the potential to be integrated into various decision-making systems. In recent years, the ability of machine learning algorithms to analyze large datasets and provide precise predictions has attracted significant attention across multiple sectors. Accurate risk assessment and fraud prevention are crucial for financial institutions to avoid potential financial losses and damage to their reputation. Machine learning algorithms have enabled financial institutions to adopt more efficient and effective strategies to mitigate these risks. Financial institutions increasingly utilize machine learning algorithms to improve risk management and fraud detection. These algorithms employ advanced statistical techniques to analyze massive datasets and identify patterns and anomalies that may indicate potential risks or fraudulent activities. The most widely used machine learning algorithms in risk management and fraud detection are supervised learning algorithms like logistic regression and decision trees and unsupervised learning algorithms like clustering and anomaly detection. These algorithms can process and analyze vast amounts of data in real time, allowing financial institutions to detect and respond more effectively to potential risks and fraudulent activities.

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Section: Challenges Associated With Implementing Machine Learning In ...mentioning

confidence: 99%

Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

Kumar,

Singh

2024

Int. J. Res. Publ. Rev

View full text Add to dashboard Cite

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Fraud detection and prevention by face recognition with and without mask for banking application

P.K,

Khaparde,

Bendre

et al. 2024

Multimed Tools Appl

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Imbalanced credit card fraud detection data: A solution based on hybrid neural network and clustering-based undersampling technique

Huang,

Liu,

Xue

et al. 2024

Applied Soft Computing

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Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

Cited by 7 publications

References 47 publications

Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

Fraud detection and prevention by face recognition with and without mask for banking application

Imbalanced credit card fraud detection data: A solution based on hybrid neural network and clustering-based undersampling technique

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