Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin
  Network for Financial Forensics
Youssef Elmougy Ling Liu
Blockchain provides the unique and accountable channel for financial
forensics by mining its open and immutable transaction data. A recent surge has
been witnessed by training machine learning models with cryptocurrency
transaction data for anomaly detection, such as money laundering and other
fraudulent activities. This paper presents a holistic applied data science
approach to fraud detection in the Bitcoin network with two original
contributions. First, we contribute the Elliptic++ dataset, which extends the
Elliptic transaction dataset to include over 822k Bitcoin wallet addresses
(nodes), each with 56 features, and 1.27M temporal interactions. This enables
both the detection of fraudulent transactions and the detection of illicit
addresses (actors) in the Bitcoin network by leveraging four types of graph
data: (i) the transaction-to-transaction graph, representing the money flow in
the Bitcoin network, (ii) the address-to-address interaction graph, capturing
the types of transaction flows between Bitcoin addresses, (iii) the
address-transaction graph, representing the bi-directional money flow between
addresses and transactions (BTC flow from input address to one or more
transactions and BTC flow from a transaction to one or more output addresses),
and (iv) the user entity graph, capturing clusters of Bitcoin addresses
representing unique Bitcoin users. Second, we perform fraud detection tasks on
all four graphs by using diverse machine learning algorithms. We show that
adding enhanced features from the address-to-address and the
address-transaction graphs not only assists in effectively detecting both
illicit transactions and illicit addresses, but also assists in gaining
in-depth understanding of the root cause of money laundering vulnerabilities in
cryptocurrency transactions and the strategies for fraud detection and
prevention. Released at github.com/git-disl/EllipticPlusPlus.