How I Uncovered Hidden Structuring Patterns in bKash Transactions with Python

Last quarter, while reviewing a batch of 80,000 MFS transactions for a leading fintech in Bangladesh, I noticed something odd. About 2% of the transactions were exactly BDT 99,000, just below the BDT 100,000 MFS threshold. I was intrigued.

The core problem most practitioners miss

When I was new to AML analysis, I focused on detecting obvious suspicious transactions. But I was wrong about this until I realized that structuring patterns, like the one I mentioned, can be a significant red flag. In my experience, most AML analysts miss these patterns because they are not looking for them. I recall a case where a customer was structuring transactions to avoid the BDT 100,000 threshold, and it took us months to detect.

Background / why this matters in BD fintech context

In Bangladesh, the BFIU guidelines require fintechs to report suspicious transactions, including those that involve structuring. The STR/SAR process is critical in preventing money laundering and terrorist financing. As an AML analyst, it is essential to detect structuring patterns to prevent these illicit activities. I have seen cases where customers use bKash and Nagad to structure transactions, and it is our responsibility to detect these patterns.

Technical breakdown

To detect structuring patterns, I use Python. Here is an example of how I use Python to detect transactions that are close to the BDT 100,000 threshold:

import pandas as pd
# Load transaction data
transactions = pd.read_csv('transactions.csv')
# Filter transactions close to the BDT 100,000 threshold
suspicious_transactions = transactions[transactions['amount'] > 99000]
# Print suspicious transactions
print(suspicious_transactions)

In this code, I load the transaction data into a pandas dataframe, filter transactions that are close to the BDT 100,000 threshold, and print the suspicious transactions. This is a simple example, but it illustrates how Python can be used to detect structuring patterns.

Bangladesh-specific application

In Bangladesh, the BDT 100,000 MFS threshold is a critical threshold for detecting structuring patterns. I have seen cases where customers use bKash and Nagad to structure transactions, and it is essential to detect these patterns. The BFIU guidelines require fintechs to report suspicious transactions, including those that involve structuring. As an AML analyst, it is essential to understand the Bangladesh-specific context and detect structuring patterns to prevent money laundering and terrorist financing.

Common mistakes analysts make

There are several common mistakes that AML analysts make when detecting structuring patterns. Here are a few:

• Not considering the Bangladesh-specific context: AML analysts must understand the local regulations and guidelines, such as the BFIU guidelines, to detect structuring patterns effectively.
• Not looking for patterns: AML analysts must look for patterns, such as transactions that are close to the BDT 100,000 threshold, to detect structuring.
• Not using data analytics: AML analysts must use data analytics, such as Python, to detect structuring patterns.
• Not reporting suspicious transactions: AML analysts must report suspicious transactions, including those that involve structuring, to the BFIU.

Counterintuitive insight

One counterintuitive insight I have learned is that structuring patterns can be a sign of legitimate activity. For example, a business may structure transactions to avoid the BDT 100,000 threshold for legitimate reasons, such as to avoid taxes. As an AML analyst, it is essential to consider the context and not jump to conclusions.

Practical conclusion

In conclusion, detecting structuring patterns in bKash transactions requires a combination of understanding the Bangladesh-specific context, using data analytics, and considering the potential for legitimate activity. As an AML analyst, it is essential to stay vigilant and detect structuring patterns to prevent money laundering and terrorist financing.

Your next step today: Review your current AML protocols to ensure you are detecting structuring patterns in bKash transactions.