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ABSTRACT:
Fraudulent banking operations can cause huge losses to the bank and further
affect the economy negatively. What if Block chain Technology and Machine Learning
could be combined to detect suspicious banking activity and stop transactions at the
source? That is what this paper aims to do is to implemented the block chain is to
securely store transaction history, For quick and efficient detection of outliers, which
indicate suspicious activity by algorithm host by machine learning. Even a single
fraudulent action has a negative impact on the economy and affects all citizens
negatively. This is why we must take a stand to prevent fraudulent banking activities.
One method of doing this is to employ Artificial Intelligence, particularly Machine
Learning, in the banking sector. We then combine it with Block chain technology to
ensure secure banking transactions in the future. This will make fraud detection quick,
easy and more efficient. The Private Permissioned Block chain contains all data
regarding the transactions and can be retrieved in real time. A reinforced K-means
clustering algorithm is applied to the block chain to detect discrepancies and point out
fraudulent transactions. The Apache ignite Platform offers powerful computing that
enables the process to occur in real time. Access control tends to be more flexible, and
is easier to implement. When large data is transmitted to the cloud, a security issue
emerges. Most organizations would not want their data in the hands of another
organization, thus the need for encryption. When the data is transmitted, it is not
encrypted because the approaches used to transmit the data require that the data be
decrypted. This exposes the data to attacks. Confidentiality breach is the biggest threat
to big data thus the encryption could be used as the primary big data protection
technique.
