This notebook presents a thorough analysis of anti-money laundering (AML) strategies using Python. It demonstrates data handling, exploratory data analysis, and the application of statistical methods to determine optimal thresholds for transaction monitoring.
- Introduction
- Dataset Description
- Installation
- Usage
- Methodology
- Results and Discussion
- Conclusion
- References
- Contact Information
Increasingly, financial institutions are exploring the use of Artificial Intelligence (AI) and Machine Learning (ML) to identify suspicious transactions related to money laundering. However, deploying AI and ML presents challenges, including regulatory, budgetary, and data quality concerns, as well as a lack of necessary expertise.
As a result, traditional rule-based or behavior-based transaction monitoring systems remain prevalent within the industry. Many financial institutions continue to rely on these as the primary method for detecting suspicious money laundering activities.
Despite the potential for AI or ML to outperform traditional systems in detecting such activities, the industry is not yet in a position to fully transition to these technologies. Therefore, regular and thorough system calibration remains essential to protect against money laundering threats.
The objective of this notebook is to optimize the true positive rate while minimizing the false positive rate. This involves several key adjustments:
- Segmenting Customers or Products
- Identifying Red Flags
- Quantifying Red Flags (Scenario Development)
- Tuning Thresholds
All these elements are crucial for the effectiveness of the monitoring system, but threshold tuning often proves to be the most time-consuming and challenging part of the calibration process. In this article, we will discuss using a statistical approach to fine-tune the threshold for any monitoring scenario.
The dataset used in this notebook is sourced from Kaggle and represents synthetic financial transactions. The dataset can be accessed here.
Instructions to set up the Python environment and install necessary libraries:
pip install pandas matplotlibTo run this notebook:
- Ensure Python and necessary libraries are installed.
- Download the dataset and place it in the appropriate directory.
- Execute the notebook cells sequentially to replicate the analysis.
This section describes the analytical methods and statistical techniques applied, including data preprocessing, visualization, and threshold analysis for identifying suspicious activities.
Key findings from the analysis are discussed here, focusing on the impact of different threshold settings on the detection of fraudulent transactions.
I adopted a statistical technique to find the best threshold for specific conditions through these steps:
- Scenario Reproduction
- Data Division (Train-test split)
- Threshold Simulation (Iteratively changing the threshold throughout the percentile range)
- Threshold Assessment (Using metrics such as the J statistic and F1 score)
- Threshold Confirmation (Applying the test set)
Though the primary focus has been on steps 2 to 5, the importance of accurately reproducing the scenario cannot be underestimated. This initial step is crucial as it lays the groundwork for the calibration project and often consumes the most time, especially when fine-tuning a complex scenario involving behavior with multiple parameters across different periods.
Additionally, those with machine learning expertise will find this process reminiscent of launching a machine learning system. This method is indeed inspired by machine learning classification challenges, where the scenario is viewed as a classifier (akin to an ML model) and the threshold as a model hyperparameter.
For more information, reach out via email or GitHub.