Fighting money laundering is a complicated task with substantial costs and risks, including—but not limited to—regulatory, reputational, and financial crime risks. Money laundering can be difficult to track, with many false alerts making detection even more challenging. But anti-money laundering machine learning models based on big data can increase detection rates and keep your firm safe.
Anti-Money Laundering (AML) models are designed to help identify suspicious activity that needs special attention. The models also support routine daily processes of financial institutions like account opening, payments, or account management as the model monitors all customer transactions.
Additionally, a well-designed AML model increases overall productivity and efficiency by making regulatory requirements easier to implement. This in turn improves the quality of AML audits, leading to more accurate predictions and risk identification.
In short, a good AML model creates a positive feedback loop for your entire institution.
In the early days, anti-money laundering models were based on qualitative, expert judgments. Today, while these models still use expert judgment, they also rely on highly developed scoring algorithms with quantitatively derived components like segments and thresholds. A good AML model should clearly define money-laundering scenarios, types, and methods as financial institutions do.
The continual improvement of modeling methods and their broader application contributes to the astonishing growth of the AML industry. In order to get the best results, you should keep an eye out for the many frequent improvements to the field.
In order to have an effective model to manage risks, you must identify all possible AML threats that your firm could be exposed to. There are four main sources of risks:
Once all potential risks have been identified and analyzed, a risk control plan can be designed.
The model should also take into consideration all other factors that will affect the business’s exposure to risks, like embargoes and sanctions requirements.
Some of the models do not cover the risk factors sufficiently. This may lead to some potential high-risk customers being tagged as medium or low risk. Thus, all monitoring systems and analytics require data quality management. All of the data collected, and the analytics that make it useful, are important resources for making more timely risk management decisions based on consistent and accurate metrics.
The most common problem of the AML model is the creation of excessive volumes of false positive cases. The risk assessment models are based on classifications of basic customer risk criteria including geography, business, and entity type. These data points are collected and embedded at the start of a customer relationship to create an initial customer risk score. However, this methodology sometimes leads to misjudgment of true customer risk that could end up classifying the customer as higher-risk than they really are. Such a mistake undermines the efficiency of the model and can lead to higher risk exposure for the financial institution.
However, by performing effective model tuning, you can keep the system efficient—and even improve it. In fact, once the AML software and data feeds are in place, there is always room for a tuning process.
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