Strengthen Your AML Program With Data Mining (II)
Recently we hosted a webinar with Symptai Consulting’s Business Analyst Rory Barrett. Titled ‘Strengthen Your AML Compliance Program with Data Mining’, the webinar discussed different data mining techniques that can be used to improve AML compliance programs. In case you missed it, you can read part 1 of our webinar recap here, and in part 2 below we discuss some of the different data mining techniques and how they can benefit your organization.
Types of data models
There are many types of data mining techniques available today. Here are a few that can be especially useful to AML compliance programs.
The first type of data model we’re going to look at is anomaly detection. This allows you to look at any group of data, whether it be customer data, transaction data or both, and the tool will analyze the information you feed it to identify patterns. The patterns are based on all of the different data elements that you provide to the model. The more information you feed it, the better and more precise your results will be. These patterns will show you what is normal or expected behavior based on what the data says, and will reveal outliers—anomalies—to be investigated.
This model is frequently used in fraud investigations because of its ability to quickly identify unusual behavior without requiring you to tell it the concrete definition of fraud. Fraud is simply identified by using the patterns. If the data that you feed this model changes, so too will what it considers to be normal patterns of behavior. Now you’re looking for not just one but various types of fraud simply based on what can be considered fraudulent or non-fraudulent behavior.
Clustering is very similar to anomaly detection in terms of its ability to pattern much, but rather than singling out anomalies, it simply shows you how certain networks or groups are related based on the data you feed it—unbiased and purely data driven. The clusters that form could represent any number of different patterns or related groups. Clustering is very effective at performing things such as crime analysis to identify patterns in where, when or how different occurrences of crimes happen. With this information, you can make more informed decisions on how to treat different regions based on the different types of criminal activity. You could also use it to devise risk mitigation strategies.
To begin predicting where and when certain types of financial crimes can occur, we have neural nets (also known as neural networks). This model is designed to help predict decisions based on past behavior. The machine learns from the data you feed it and improve its performance based on pattern matching. Using machine learning, you can take what elements you already know about fraudulent activity and those customers and create a model that can actually predict those incidents before they occur. These are particularly useful in detecting fraud early while also minimizing false-positives because instead of using thresholds and barriers to detect fraud, you’re using patterns. When a fraudster looks to change their tactics, richly trained neural nets can not only detect but adapt to the changes in behavior.
There are many benefits to data mining, including:
- Savings of time, manpower and costs
- Solving problems in real-time
- Predicting fraud
- More accurate data
- Better informed decisions
- Faster information for faster action
- Thousands of risk patterns being analyzed instantly
Applications in industry
Data mining is a powerful tool for detecting fraud, but it’s also being used in almost every industry today. Wherever there is data, there can be an application for data mining. For example, data mining is being used in healthcare to help diagnose illness by detecting patients with similar symptoms and the outcomes of those patients. Data is now not only simply being collected, it’s being used to enrich our lives.
To learn more about data mining and how it can be used to improve your AML compliance program, watch the full recording of the webinar now.
About Anu Sood
Anu Sood (LinkedIn | Twitter) is the Director Marketing at CaseWare RCM and is responsible for the company’s global marketing strategy. She has over 20 years of experience in product development, product management, product marketing, corporate communications, demand generation, content marketing and strategic marketing in high-tech industries.