What question did this study set out to answer?

To introduce an intelligent cybersecurity defense mechanism combining artificial intelligence and machine learning techniques with existing fraud detection systems to identify fraudulent banking transactions.

June 30, 2022International Journal of Electrical and Electronics ResearchOpen Access

Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

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To introduce an intelligent cybersecurity defense mechanism combining artificial intelligence and machine learning techniques with existing fraud detection systems to identify fraudulent banking transactions.
Evaluated the vulnerabilities of conventional, non-dynamic algorithms against evolving financial cyber-attacks and digital currency fraud.
Integrated machine learning techniques, specifically the random forest algorithm, with Feedzai's Open ML fraud detection architecture for automated threat identification.
Established an automated framework tailored for intelligent malware categorization and dynamic threat sensing in banking transactions.
Demonstrated the application of the random forest algorithm within the Feedzai security environment to improve automated fraud recognition.

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Implication

Technical framework demonstrates machine learning integration with the Feedzai model for financial fraud detection, suggesting automated defenses outperform conventional algorithms against dynamic...

Cite This Study

Narsimha et al. (2022) studied this question.

synapsesocial.com/papers/69d95ed55e5bcb4e3b835da6 https://doi.org/10.37391/ijeer.100206

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Implication

Technical framework demonstrates machine learning integration with the Feedzai model for financial fraud detection, suggesting automated defenses outperform conventional algorithms against dynamic...

Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

Key Points

Discussion

Implication

Cite This Study

Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

Key Points

Discussion

Implication

Cite This Study

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