Weekly Challenges
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Hi Community members,
A solution to last week’s challenge can be found here.
This challenge was submitted by our ACE Ippei Nakagawa, @gawa. Thank you, Gawa, for your submission!
In this week’s challenge, your task is to detect transactions that may indicate money laundering. Money laundering schemes often follow a circular pattern, such as:
- A sends money to B
- B sends money to C
- C sends the same money back to A
These transactions form a loop, and your goal is to identify such suspicious loops in the dataset.
To qualify as potential money laundering, a group of transactions must meet all the following criteria:
- Consistent Amount: The same amount of money is transferred in each transaction forming a loop.
- Cyclic Return: The money eventually returns to the original sender, forming a complete loop.
- Chronological Order: Transactions within the loop occur in strictly ascending order of transaction date.
- Minimum Hops: A valid loop must include at least two intermediary transactions (i.e., at least three participants in total, including the original sender).
Once you have completed your challenge, include your solution file and a screenshot of your workflow as attachments to your comment.
Good Luck!
The Academy Team
Source: Randomly generated dataset using Python.
Tough challenge. I made the classic mistake and got a bit mixed up with all the different IDs, but got there in the end. Liked the use case too, great fun!
Macro:
Thank you, Gawa @gawa.
This was a tough challenge but a really good one!
I leveraged the approach from @abacon - thanks much for sharing it. This approach seemed to me a fairly simple and straightforward one. I realized the trick was to use Receiver as Sender for iteration to work in the end as the money had to return to the sender in a loop.
Iterative macro:
