Importance

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Abstract

The huge size of ever-increasing cryptocurrency data makes investigating transactions for identifying fraudulent activities challenging. Conventional Artificial Intelligence (AI) models trained on a sample cryptocurrency transaction dataset are not scalable or efficient and getting required labelled data for training the AI models is a challenge due to the pseudo-anonymity of entities in cryptocurrency transactions. The present disclosure enables generating of patterned transactions pertaining to different entities using an input specification in the form of a transaction schema that describes one or more parameters including one or more entities, a quantity of cryptocurrency transactions, time frame; and a pattern describing a typology for the cryptocurrency transactions to be generated. The input specification is processed to simulate customizable and scalable training data characterized by the behavior or nature of entities seen in the real world and associated with different patterns including the money laundering patterns described in the input specification.

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First Claim