AI-Powered Blockchain Systems for Real-Time Fraud Detection in Financial Services

The AI based blockchain system proposed in this paper targets at providing real time fraud detection in the financial services overcoming the drawbacks of conventional fraud identification approaches. The proposed solution follows a permissioned blockchain architecture combined with machine learning to allow detection of the fraudulent activities. In real-time, the gradient boosting, and the recurrent neural network models deployed in blockchain smart contracts analyze the transaction data. Simulation experiments helped to confirm a high accuracy of fraud detection, which for the most cases does not exceed 2% while the experimental blockchain network demonstrated enough speed, with the number of transactions per second being up to 1200 while latency being low enough. AI and blockchain integration outlined in the study demonstrate the possibilities of their use in improving the tools for fraud detection, and create a basis for the advancement of secure financial technologies.

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Publication Details

Published
2024-11-23
DOI
https://doi.org/10.1109/icicat62666.2024.10923418
Citations
1
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.78
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article

AI-Powered Blockchain Systems for Real-Time Fraud Detection in Financial Services

G. Merlin Sheeba, Mohsen Fallah, A. Devendran, Himanshu Sharma et al.
1 citations
Blockchain Technology Applications and Security
0.78
article

AI-Powered Blockchain Systems for Real-Time Fraud Detection in Financial Services

G. Merlin Sheeba, Mohsen Fallah, A. Devendran, Himanshu Sharma, Dharmapuri Siri, G. Ravi Kumar
article en
1 citations

Abstract

The AI based blockchain system proposed in this paper targets at providing real time fraud detection in the financial services overcoming the drawbacks of conventional fraud identification approaches. The proposed solution follows a permissioned blockchain architecture combined with machine learning to allow detection of the fraudulent activities. In real-time, the gradient boosting, and the recurrent neural network models deployed in blockchain smart contracts analyze the transaction data. Simulation experiments helped to confirm a high accuracy of fraud detection, which for the most cases does not exceed 2% while the experimental blockchain network demonstrated enough speed, with the number of transactions per second being up to 1200 while latency being low enough. AI and blockchain integration outlined in the study demonstrate the possibilities of their use in improving the tools for fraud detection, and create a basis for the advancement of secure financial technologies.

Iraqi University (IQ), Woxsen School of Business (IN), CMR University (IN), GLA University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 17%
Blockchain Technology Applications and Security
0.78
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AI-Powered Blockchain Systems for Real-Time Fraud Detection in Financial Services — G. Merlin Sheeba, Mohsen Fallah, et al. · (2024) | TGRS Research Map | TGRS