Boosting Blockchain Security with Graph Technology

Dan McGary is Senior Sales Executive for Mid-Market Enterprise East at graph database leader Neo4j

 

As blockchain-backed cryptocurrencies become an increasing part of the finance mainstream, Neo4j’s Dan McGary explains how graph technology can make sense of transaction patterns, spotting outliers and fraud

Traditional finance is seeing increased competition from blockchain and cryptocurrencies. Individuals and corporations can lend, borrow, swap, trade, and hedge on blockchain. Service providers and sectors such as oil and gas are increasingly trading in global digital currencies, backed by the security of blockchain technology.

The world’s first cryptocurrency, bitcoin, launched in 2009, and since then, almost 19 million bitcoins have been mined. The value of bitcoin and other digital currencies such as Ethereum, Tether, and BNB is based on the trust users have in the system—and that trust is based on blockchain technology. With blockchain, every transaction record is timestamped and visible to every other node in the network.

In simple terms, an anonymous user mines bitcoins and sends them from their wallets as transactions, each with an input and an output address. To support the validity of a transaction, bitcoin uses blockchain to maintain and protect the correct order of transactions in separate blocks. It shares details of each block with every other bitcoin user. The entire dataset–right back to the very first transaction—is available for everyone to see.

 

Dan McGary

Novel vectors for fraud

In this way, digital currencies are based on the high level of trust that is clearly provided by blockchain. However, despite this, every technological development brings new vectors for fraud. Data analysts are typically on the lookout for both known and unknown types of fraud. Known fraud has been encountered before and, even within billions of transactions, it can be detected with pattern matching tools. Unknown fraud, as the name suggests, has not been encountered before. It is impossible to define rules to detect new types of fraud.

The complex network of connections in the blockchain system might at first appear to obscure full visibility of transactions, leaving financial institutions open to fraud. It is a challenge to spot unusual transactions among the vast number of blocks going through the networks. Visibility is also key to regulatory compliance. Exploiting the blockchain effectively while ticking regulatory boxes is the balancing act facing businesses seeking to mine the benefits of blockchain.

The modern world is a whirlwind in which data is being created at exponential growth rates and data connections appear and dissolve dynamically. Relational databases were built for simpler times when data was stable and structured. Their rigid data models can’t be updated fast enough to meet the new needs of modern organizations. Based on the very latest in data science and software engineering breakthroughs, graph databases offer significant advantages that include:

  • Simple, natural data models that mirror your business reality and let you add as many business entities, relationships, and rules as your application requires
  • Flexibility for evolving data structures that support agile development in today’s turbulent world
  • Real-time updating while users simultaneously access graph data
  • Index-free adjacency
  • Better querying and analytics that provide faster, richer insights by exploiting the connections and context of native graph data.

Graph technology that picks up anomalies and unusual connections is vital here. It is important that organisations can connect data across siloes to get a clear picture of criminal activity. Speed of analysis is key to enabling firms to act quickly on instances of fraud and prevent greater losses.

At the same time, it is critical that banks maintain an in-depth understanding of their financial data. From money laundering to monetising ransomware, buying illegal goods to fraud scams, criminals use cryptocurrency as a secure, low-cost anonymous way to transfer funds quickly and easily.

 

Analysing bitcoin transactions at scale

Graph technology can support analysis that helps uncover irregularities. At a foundational level, blockchain is a transaction with an input, output, and value. Transactions are either automated, for example, when a cryptocurrency investor uses a trading bot to buy or sell bitcoins at the best price, or manual, when users send bitcoins from one wallet to another in exchange for goods or services.

Crypto currency fraud analysts typically look at huge volumes of historical data spanning long time periods. They focus in on six-second blocks—and this approach does make it challenging to identify whether a transaction is automated or manual. But, it is possible to uncover patterns to help understand what might be happening.

When the same bitcoin moves quickly between addresses, it forms long chains in the data. When it happens inside a short time frame, it’s likely to be a series of automated payments. A closed loop means the same account is involved in multiple transactions.

All that is very simple to encode into a graph database, with its built-in ability to handle nodes and the relationships between them. Graph databases can represent key crypto structures like Blocks, Transactions, and Addresses ready for analysis. For instance, it is possible to follow the path taken by a bitcoin transaction to see if two different addresses are connected.

 

Guarding against decentralised finance crime in Germany

Graph technology reveals instances of illegal use of decentralised finance. PwC Germany has developed BETA, the Blockchain Explorer and Transaction Analyzer, based on graph technology. With over 300,000 transactions a day on the bitcoin blockchain, and more than a million transactions a day on Ethereum, reporting posed huge challenges. One of the key features of BETA is explainable risk reporting.

BETA integrates existing AML (anti-money laundering) scoring providers, linking local data of a crypto asset service provider, such as order history, KYC records, IP session logs, and other PII data, to create uniform, transparent, transaction-risk scoring levels. In this way, it supports PwC’s crypto asset service by guarding firms against the risk of financial crime, while meeting compliance and regulatory requirements.

 

Reaping the rewards of crypto currency

Conventional financial services players are in no doubt about the potential rewards that could come from cryptocurrency, but at the same time they are highly cautious. Many are adopting a wait-and-see approach. However, newer challenger banks are already moving ahead to gain competitive advantage from digital currencies. Emerging digital- and customer-centric financial services firms are increasingly offering ways to bank using cryptocurrency to customers—and traditional banks are keen not to be left behind.

For fintechs looking to enter the cryptocurrency market, there is huge potential for graph databases to analyse blockchain and render digital currencies more transparent and secure.

 

 

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