Great talks! I would like to ask if it is possible to use anomaly detection to detect fraud in ATM transactions with the following features: CardNo, branch-code, AtmID, Trans-date, Amount, Trans-type, Trans-status How can the customer's regular transaction patterns be used to detect anomalies (suspicious fraud)?
Hi @hakeemojulari7392, I dont know if you got an answer to this but its possible. You might want to look into Deep learning architectures called Autoencoders. In theory, if you give an autoencoder the regular customer's data, you can train it to reconstruct every single datapoint. What hapens as a result is that it learns patterns regarding normal transaction behavior, and the reconstruction loss gets smaller and smaller. You can then use the reconstruction loss of the autoencoder to detect suspicious transactions. Intuitively, if a transaction is so new that the autoencoder has never seen before, the reconstruction loss will be higher that a transaction that it has seen before therefore flagging it.
Can it also be said the anomaly detection model can used to label data that can be used in supervised learning model that can be used to for fraud detection?
Great talks! I would like to ask if it is possible to use anomaly detection to detect fraud in ATM transactions with the following features:
CardNo, branch-code, AtmID, Trans-date, Amount, Trans-type, Trans-status
How can the customer's regular transaction patterns be used to detect anomalies (suspicious fraud)?
Hi @hakeemojulari7392, I dont know if you got an answer to this but its possible. You might want to look into Deep learning architectures called Autoencoders. In theory, if you give an autoencoder the regular customer's data, you can train it to reconstruct every single datapoint. What hapens as a result is that it learns patterns regarding normal transaction behavior, and the reconstruction loss gets smaller and smaller. You can then use the reconstruction loss of the autoencoder to detect suspicious transactions. Intuitively, if a transaction is so new that the autoencoder has never seen before, the reconstruction loss will be higher that a transaction that it has seen before therefore flagging it.
Thank you
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Can it also be said the anomaly detection model can used to label data that can be used in supervised learning model that can be used to for fraud detection?
What is require feature for anomaly detection?
Can you share the code?
Please share the
code