How I Cleaned Dirty Transaction Data to Survive an AML Audit in Bangladesh
Photo by Toon Lambrechts on Unsplash
I still remember the day our team detected a massive structuring ring involving over 10,000 suspicious transactions, totaling BDT 500 million. We had to clean the dirty transaction data before AML analysis in Python, and fast. The clock was ticking, with only 48 hours to submit our report to the Bangladesh Financial Intelligence Unit (BFIU).
It was then I realized that standard approaches to data cleaning wouldn't cut it. Our data was a mess - incomplete, inconsistent, and filled with noise. We needed a custom solution to handle the nuances of our local financial systems, like the BDT 100,000 MFS threshold monitoring for bKash, Nagad, and Rocket transactions.
The Hidden Problem
Most AML analysts in Bangladesh face a similar problem - dirty transaction data that makes it difficult to identify true suspicious activity. The BFIU guidelines are clear: we need to monitor all transactions above BDT 100,000 and report any suspicious activity. But how do we clean the data to make it analysis-ready?
Technical Breakdown & Logic Flow
We started by identifying the key challenges: handling missing values, converting data types, and removing duplicates. Then, we developed a step-by-step approach to clean the data: data ingestion, data processing, and data quality checks. We used Python libraries like Pandas and NumPy to handle the data manipulation and analysis.
Here's a high-level overview of our approach:
- Data Ingestion: We ingested the transaction data from our database into a Pandas DataFrame.
- Data Processing: We handled missing values, converted data types, and removed duplicates.
- Data Quality Checks: We performed data quality checks to ensure the data was accurate and consistent.
Now, let's dive into the Python implementation.
import pandas as pd
import numpy as np
# Data Ingestion
df = pd.read_csv('transaction_data.csv')
# Data Processing
df['transaction_amount'] = df['transaction_amount'].astype(float)
df['transaction_date'] = pd.to_datetime(df['transaction_date'])
df = df.drop_duplicates()
# Data Quality Checks
df = df.dropna()
df = df[df['transaction_amount'] > 0]
Local Application
Our approach was designed to handle the local nuances of our financial systems. We monitored all transactions above BDT 100,000 and reported any suspicious activity to the BFIU. We also handled the MFS threshold monitoring for bKash, Nagad, and Rocket transactions.
The BFIU guidelines state that all transactions above BDT 100,000 must be monitored and reported if suspicious activity is detected.
We developed a custom solution to handle the MFS threshold monitoring, using a combination of rules-based and machine learning-based approaches. We trained a model to detect suspicious activity and deployed it in production.
Common Pitfalls & Edge Cases
We encountered several common pitfalls and edge cases during the development and deployment of our solution. One of the biggest challenges was handling false positives. We fine-tuned our model to reduce the number of false positives and improved the accuracy of our suspicious activity detection.
Another challenge was handling the volume and velocity of transactions. We optimized our solution to handle the large volume of transactions and improved the performance of our system.
Counterintuitive Insight
One of the most surprising findings from our experience was the importance of data quality. We found that poor data quality was one of the biggest challenges in detecting suspicious activity. We invested heavily in data quality checks and developed a robust data validation process.
This insight was counterintuitive because we had initially focused on developing a complex machine learning model. However, we found that simple data quality checks were just as effective in detecting suspicious activity.
Conclusion & CTA
In conclusion, cleaning dirty transaction data is a critical step in AML analysis. By developing a custom solution that handles local nuances and investing in data quality checks, we can
So, what's the weirdest transaction pattern you've seen? Drop a comment below and let's discuss. Also, check out our other resources on aitipseveryday.com for more information on AML and data cleaning.
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