Blockchain-based currencies or cryptocurrencies have become a global phenomenon known to most people as a disruptive technology, and a new investment vehicle. However, due to their decentralized nature, regulating these markets has presented regulators with difficulties in finding a balance between nurturing innovation, and protecting consumers. The growing concerns about illicit activity have forced regulators to seek new ways of detecting, analyzing, and ultimately policing public blockchain transactions. Extensive research on machine learning, and transaction graph analysis algorithms has been done to track suspicious behaviour. However, having a macro view of a public ledger is equally important before pursuing a more fine-grained analysis. Benford’s law, the law of first digit, has been extensively used as a tool to discover accountant frauds (many other use cases exist). The basic motivation that drove our research presented in this paper was to test he applicability of the well established method to a new domain, in this case the identification of anomalous behavior using Benford’s law conformity test to the cryptocurrency domain. The research focused on transaction values in all major cryptocurrencies. A suitable time-period was identified that was long enough to sport sufficiently large number of observations for Benford’s law conformity tests and was also situated long enough in the past so that the anomalies were identified and well documented. The results show that most of the cryptocurrencies that did not conform to Benford’s law had well documented anomalous incidents, the first digits of aggregated transaction values of all well known cryptocurrency projects were conforming to Benford’s law. Thus the proposed method is applicable to the new domain.