To hide money, criminals are using cryptocurrency. Bitcoin mixing services hide the sender-receiver relationship to make fraudulent transactions harder to trace. Agents in money laundering are crucial to understanding the mysterious world of bitcoin mixing services. In this paper, we model, discover, and evaluate several roles in the Using historical bitcoin transaction data, we can simulate a situation in which bitcoin is mixed with other currencies. Agents’ motivations in bitcoin money laundering are explored. It also gives a foundation for examining actual bitcoin money launderers. The bitcoin-prime.app is an online investment platform that has been operating for quite some time to provide very useful information.
Making bitcoin transactions is an easy way to launder bitcoins. Criminals frequently use Bitcoin pseudonyms to conceal their funding sources. In order to avoid revealing users’ pseudonyms, third-party bitcoin mixing businesses have sprouted up. After the Binance hack, at least 4,836 bitcoins were reportedly laundered using a crypto mixing service.
Bitcoin Transaction Data Analysis
The exchange of bitcoins among two addresses is known as a bitcoin transaction. This is an unsourced output address from another transaction. Using Bitcoin’s unique hash technique, virtual values may be created offline to a public key an example of a basic UTXO There are three steps. In Transaction, a gets 10 BTC, while B, C, and D each receives 10.
Cryptographic transparency allows for statistical and graphical analysis. Unified overview of essential bitcoin transaction analysis operations Network risk parsing, network visualization, and portraiture, and market analysis. Included are transaction monitoring, blockchain address linkage, user activity analysis, and user profiling.
Cryptocurrency addresses used during transactions may be compromised in a variety of ways. Classification methods based on transaction graph characteristics, such as a near neighbor, deep walk integration, node2vc embedding, and tree-based method, were proposed in their paper. Once an address is known, Bitcoin’s privacy is gone.
Bitcoin traceability issues
Due to bitcoin’s original nature, several methods have been proposed to strengthen its anonymity. On the Silk Road, money laundering is facilitated by combining services. Mixing services attempt to overcome cryptocurrencies’ traceability issues by exchanging and conjoining unrelated transactions. Only a few prior researches have tried to dissect mixing services. Early mixing service studies used basic network analysis to account for ambiguous genuine identity knowledge. Claim that a bitcoin mixing service can spot money laundering.
Money Laundering: Goal Modeling and Data Mining Method
The role of agents in money laundering can be represented using goal-oriented modeling. We assume that various actors play distinct roles in money laundering using goal modeling and mining. It has three phases. For each address, this phase will collect bitcoin transaction data as well as domain data. The model finding phase uses the address, role, and process miners. It gathers all address data. Agent objectives will be discovered using role mining.
The blockchain analysis phase includes sub-objectives such as positioning, mounting, and integrating. Closing the money laundering loopholes. Bringing in illicit monies may help. Sub-tasks include money deposit and money remit. Large sums of cash must first be split. Soldiers will be given chores to avoid attention.
Money is transferred between accounts or organizations to disguise its source. Using layers to obscure money’s origins. Chores include money transfers and insurance purchases. Money is currently moving. However, communicators manage money and data.
This is lawful economy cash. Legalizing illegal funds may promote integration. This activity includes transferring money between banks, withdrawing money, and investing it. They will then hold the legalized funds. Communicators and organizers will do the job.
Using Data Mining to Combat Money Laundering
Terrorism, narcotics and weapon smuggling, and human trafficking are all related to money laundering. A data mining approach may assist identify money laundering. Employing data mining, the research on anti-money laundering and detecting suspicious transactions was assessed.
Machine learning and rule-based categorization are often used in data mining. Rule-based approaches categorize questionable transactions using ontologies. Ontology gives more information about suspect transactions due to native reasoning support. A framework for anti-money laundering was built using ontologies and rule-based planning. Money laundering received a Bayesian risk score. It is based on a State Bank of Pakistan policy from 2008.
Machine learning algorithms were employed to categorize data and predict suspicious money laundering activities. For suspicious transaction data filtering, an SVM–based classification system was developed. Although data-dependent, machine learning is neither flexible or scalable. This makes it difficult to discover immigrants’ illegal actions.