Building Effective AML Systems with Python in Bangladesh
Photo by Trnava University on Unsplash Anti-money laundering (AML) is a critical aspect of fintech compliance in Bangladesh and South Asia, with regulatory bodies such as the Bangladesh Financial Intelligence Unit (BFIU) and the Asia/Pacific Group on Money Laundering (APG) working to prevent money laundering and terrorist financing. Detecting Suspicious Activity with Python Python is a popular programming language used in AML systems for its simplicity and flexibility. AML analysts can use Python libraries such as Pandas and NumPy to analyze large datasets and identify suspicious activity. For example, df = pd.read_csv('transactions.csv') can be used to read a CSV file containing transaction data, and then df['amount'].describe() can be used to generate summary statistics on the transaction amounts. Implementing Rule-Based AML Systems Rule-based AML systems use predefined rules to identify suspicious activity. These rules can be based on factors such as transaction a...