Fortifying Financial Integrity: Advanced Fraud Detection Techniques for Business Security

This research paper explores in depth on enhanced fraud detection methodologies in order to enhance security of business on financial transactions using AI, machine learning and blockchain technologies. Based on a set of financial transactions the paper identifies the pattern of frauds and measures the efficiency of various anti-fraud measures. The results thus show that AI and machine learning enhance the level of accuracy of the detection of fraud while at the same time reducing the number of false positives. These are suggested to be implemented with existing systems in the following effective manner: The findings of this work can help organizations interested in further upgrading their security and counteracting new fraud strategies. The study thus highlights the need for, and value of obedience to, frequent checks and training of employees and most importantly staying in compliance with financial laws. The last section of the work is focused on the set of recommendations for future research pertinent to the development of more sophisticated methods for fraud identification within various fields combined with an introduction to quantum computing

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-30
DOI
https://doi.org/10.5281/zenodo.15187286
Primary Topic
Internet of Things and AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Fortifying Financial Integrity: Advanced Fraud Detection Techniques for Business Security

Zenodo (CERN European Organization for Nuclear Research)
Internet of Things and AI
article

Fortifying Financial Integrity: Advanced Fraud Detection Techniques for Business Security

article en

Abstract

This research paper explores in depth on enhanced fraud detection methodologies in order to enhance security of business on financial transactions using AI, machine learning and blockchain technologies. Based on a set of financial transactions the paper identifies the pattern of frauds and measures the efficiency of various anti-fraud measures. The results thus show that AI and machine learning enhance the level of accuracy of the detection of fraud while at the same time reducing the number of false positives. These are suggested to be implemented with existing systems in the following effective manner: The findings of this work can help organizations interested in further upgrading their security and counteracting new fraud strategies. The study thus highlights the need for, and value of obedience to, frequent checks and training of employees and most importantly staying in compliance with financial laws. The last section of the work is focused on the set of recommendations for future research pertinent to the development of more sophisticated methods for fraud identification within various fields combined with an introduction to quantum computing

Zenodo (CERN European Organization for Nuclear Research)
University of Phoenix (US), University of Washington (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 37%
Internet of Things and AI
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Fortifying Financial Integrity: Advanced Fraud Detection Techniques for Business Security · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS