Let's say we have a case of money laundering detection, and the only identification for customer and business is their bank_account numbers. How can we encode them for the input to neural networks. Onehot encoding can be too sparse.
Thanks!
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1$\begingroup$ Use an embedding layer. $\endgroup$– Sycorax ♦Commented Feb 9, 2022 at 13:20
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3$\begingroup$ Why do you think those would be useful as a features? In general, things like user ID's are usually not useful as features. $\endgroup$– TimCommented Feb 9, 2022 at 13:29
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$\begingroup$ @Tim, how else would you find anomalous behavior for a specific person? $\endgroup$– Saif Ali KhanCommented Feb 9, 2022 at 18:38
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1$\begingroup$ @SaifAliKhan by having a model that gives the person some kind of score. With user ID as a feature, the model cannot generalize to people that were not in your training set. Moreover, I assume it is a supervised learning problem, so you need to have those users tagged--in such a case, you already know they are fraudulent, so don't need the model, just use your labels to blacklist them. $\endgroup$– TimCommented Feb 9, 2022 at 18:47
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1$\begingroup$ You'd have to do an experiment to find out if it solves your specific problem satisfactorily. If you don't want to do an experiment, you'll have to trust the research produced by others. What have you learned by reading prior papers that use NNs to detect money laundering? $\endgroup$– Sycorax ♦Commented Feb 9, 2022 at 19:01
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