Machine learning is a critical feature of effective anti-fraud solutions. When an anti-fraud solution can learn, it builds more effective algorithms over time. As it analyzes transactions, it improves its approach. It learns to identify signs of fraud more quickly and effectively, and it reduces its false-positive rate.
How can machines learn? There are several different types of machine learning. Here are five of the most common types of machine learning used in anti-fraud solutions.
1. Peer-group analysis
Machine learning anti-fraud solutions use peer-group analysis to learn about the behavior of specific subgroups of customers. For example, the anti-fraud solution may create profiles of all banking customers who go on vacation during the winter holidays.
Then, it can analyze these groups of peers and learn how they typically act. If someone in this peer group is behaving in ways that deviate from the expected behavior, the solution knows there is a heightened fraud risk, and it can respond accordingly.
2. Markov models
Rule-based fraud detection solutions tend to focus on just a few elements of a transaction. For instance, they may flag a transaction as potentially fraudulent if it is over a certain amount or if it occurs within a certain time frame of a customer opening a new account.
The Markov model allows financial institutions to get past the limitations of rule-based fraud detection. Rather than looking just at a single rule or element, it analyzes how people progress through a series of steps.
For example, an algorithm using the Markov model may understand the typical pattern legitimate banking customers use when initiating a transfer. Or, it may look at the patterns used in internal workflows to watch for signs of internal fraud.
Once the anti-fraud solution uses the Markov model to learn about the progression of steps in legitimate transactions or workflows, it can effectively analyze these processes for signs of fraud. Again, the solution doesn’t just apply this model once. Instead, it uses it to continuously improve its ability to detect fraud.
3. Random forests
Random forests are collections of decision trees, and they can detect errant activity in a very nuanced and extremely accurate way.
A decision tree is a series of questions that lead to a range of possible outcomes. Decision trees tend to classify information in a relatively simple, linear format. But when combined into a random forest, they become a much more effective force at detecting fraud.
4. Neural networks
Neural networks attempt to approximate the structure of the human brain. These networks are given large amounts of historic data, and they use that information to learn about legitimate and fraudulent behavior.
Again, however, this is about machine learning. When network networks make a mistake, they reroute the connections in their “neurons”. Then, they take this new knowledge into account when classifying actions as legitimate or fraudulent in the future.
5. Entity-link analysis
Entity-link analysis looks for collusion between bank employees and external actors. It analyzes signs of a relationship between an employee and outsiders. This can be a valuable resource for detecting external fraud with internal roots.
For example, this tool may find cases where an employee routinely accesses friends and relatives’ accounts to remove charges.
At SQN Banking Systems, we provide our clients with fraud detection and prevention solutions that leverage machine learning. We are committed to helping our clients detect fraud as effectively as possible, while also not adding any unnecessary friction to the customer experience. Machine learning drives these capabilities.
To learn more about how our anti-fraud tools and solutions can help your financial institution, contact us today.