Want to fight crypto crime like a pro? This advanced guide dives into the world of AML (Anti-Money Laundering) forensics in crypto, revealing how hackers launder money and how you can trace and prevent illicit activities. Learn cutting-edge techniques to navigate the dark web, analyze blockchain transactions, and stay ahead of cybercriminals in 2025.
What You'll Learn: ✅ How hackers use crypto laundering techniques to hide illicit funds ✅ AML forensic tools and methods for tracing suspicious transactions ✅ How to analyze blockchain data to uncover money laundering patterns ✅ Strategies for preventing and reporting crypto-related crimes ✅ Real-world case studies of crypto forensics in action
Why This Matters: As crypto adoption grows, so does the risk of illicit activities. Whether you're a compliance professional, law enforcement officer, or crypto enthusiast, this guide will equip you with the skills to protect the integrity of the crypto ecosystem.
Who is this for?
Compliance and AML professionals
Law enforcement and cybersecurity experts
Crypto investors and enthusiasts
Anyone interested in blockchain forensics
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and then on the parity trace.
We will see the same information as in EtherScan, only raw and unindexed.
We can see the input data sent without decoding.
We can see the addresses to and from, whether the broadcast was transmitted and other parameters.
In this particular transaction, the
Another useful analytics tool for us is the Tenderless service.
It allows us to highlight the sections of code used in a transaction at the smart contract level.
For example, let's take our analyzed transaction and insert it into Tenderly.
We will be able to see exactly the same information in a different format that EtherScan provides.
As well as the functions and methods that were used during the transaction call.
We can change the call representation by switching the interface between function tracing and call tracing.
If we switch to call tracing, we will see an interface where each new line will represent an internal smart contract call.
The Tenderless service will be useful to us in analyzing complex transactions
and to understand what functionality was included when creating a smart contract.
This uses the same addresses to mix cryptocurrencies from different users.
If you identify these addresses as intermediate addresses once,
you can quite easily identify the incoming and outgoing addresses of the money laundering organizations.
Therefore, to analyze staking schemes,
it is necessary to understand that the addresses involved in staking may not be directly related to each other.
And the analyst needs to understand the protocol of various staking mechanisms by participants.
But the direct connection between the sender and the recipient is hidden thanks to the Eternity Cache mechanisms.
Let's take and analyze the Eternity Cache pool that works with the smallest amount in EDH.
Since the pool is a smart contract, we should look at incoming and outgoing transactions,
If we open the call schedule and look closely, we will see that the arbitrageur used full liquidity pools,
but there were two separate arbitrages.
The WethPOP and WethTest token pairs were used.
An arbitrageur can use several pools on one decentralized exchange
or on centrics and calculations that allow you to see an expanded picture.
The dashboard displays information about the trading platforms for which NFT tokens are laundered
and displays the amount of laundering.
You can see whether a certain NFT has been used in fraudulent activities or not,
as well as get generated statistics on the current state of money laundering using NFT tokens.
To analyze the behavior of a particular NFT,
you can use the following SQL query as a basis, which will provide a lot of useful information.
If we open an arbitrary transaction in which the reading local method was called,
then analyzing the parameters of the called method,
we will see in which blockchain the user will receive his tokens, their number, as well as the recipient's address.
As we can see, there is no exchange event,
which can further mislead analysts who analyze only transfers between blockchains.