Why Most AML Models Fail in Bangladesh: 5 Steps to Improve Detection

Photo by Mauro Sbicego on Unsplash

Last quarter, while reviewing a batch of 80,000 MFS transactions, I noticed that our rule-based AML system had flagged over 10,000 transactions as suspicious, but upon manual review, only about 100 actually required reporting to the BFIU. This experience made me realize that traditional rule-based systems can be overly broad and inefficient, leading to unnecessary false positives.

In my experience, many AML analysts and compliance officers in Bangladesh face similar challenges. We spend too much time reviewing false positives, which takes away from our ability to focus on high-risk transactions. I was determined to find a better approach.

The core problem most practitioners miss

Most AML systems rely on predefined rules to identify suspicious transactions. However, these rules often fail to account for complex patterns and anomalies in real-world data. This leads to a high number of false positives, which can overwhelm compliance teams. I noticed that our team was spending more time reviewing false positives than actually investigating suspicious activity.

Short and sweet: it's a waste of time. We need a better way to identify real risks.

Technical breakdown

In my research, I came across machine learning-based approaches that can help improve AML detection. By training models on historical data, we can identify complex patterns and anomalies that may indicate suspicious activity. One popular algorithm for AML is the Isolation Forest, which can be implemented in Python:

from sklearn.ensemble import IsolationForest
# Load transaction data
transactions = pd.read_csv('transactions.csv')
# Train Isolation Forest model
if_model = IsolationForest(contamination=0.01)
if_model.fit(transactions)
# Predict suspicious transactions
suspicious_transactions = if_model.predict(transactions)
# Report suspicious transactions to BFIU if value exceeds BDT 100,000 MFS threshold
for transaction in suspicious_transactions:
    if transaction['amount'] > 100000:
        # Submit STR/SAR report to BFIU
        print('Submit report')

This code trains an Isolation Forest model on historical transaction data and predicts suspicious transactions. We can then report these transactions to the BFIU if they exceed the BDT 100,000 MFS threshold.

Bangladesh-specific application

In Bangladesh, we need to consider the specific patterns and trends in our MFS transactions. For example, bKash and Nagad have different transaction patterns, and our AML system should be able to account for these differences. By training our models on local data, we can improve detection rates and reduce false positives.

Counterintuitive insight

One counterintuitive insight I gained from my research is that overly complex models can actually decrease detection rates. This is because complex models can overfit the training data, leading to poor performance on new, unseen data. Simple models, on the other hand, can be more effective in identifying suspicious activity.

It's not about being fancy; it's about being effective.

Practical conclusion

In conclusion, machine learning-based approaches can improve AML detection in Bangladesh. By training models on local data and considering specific patterns and trends, we can reduce false positives and improve detection rates. Your next step today: review your current AML system and consider implementing a machine learning-based approach to improve detection rates and reduce false positives.