Fraud detection and prevention tools rely on machine learning to improve their efforts. The more the tools learn, the more effectively they can spot signs of fraud and avoid false-positives. But how does machine learning work? How can anti-fraud tools be programmed to get smarter the longer you use them? The answer is algorithms.
Machine learning uses algorithms that can be classified into supervised and unsupervised learning, and there are several different ways for programs to analyze data using both of these learning methods. To help you get a better understanding of the process, this guide explains the basic structure of the algorithms used to spot fraud.
Supervised Machine Learning Algorithms
With supervised learning, the machine learns about complex data patterns by analyzing existing data that has been labeled as fraud by a human. The machine extrapolates signs of fraud based on what it sees in these existing data sets.
Supervised learning algorithms are either based on classification or regressions. Classification models predict the probability of a certain action being fraud based on the data they have observed. Regression models predict a quantity output. Here are the main algorithms in this category:
Logistic regression learning looks at cause-and-effect relationships between varying elements in a data set. Then, it creates algorithms to determine the likelihood of a transaction being fraudulent or legitimate based on these relationships.
Decision trees involve the creation of a set of rules that show how a legitimate customer progresses through a normal transaction. The machine watches the actions legitimate customers make, and it looks for aberrations to expected patterns.
Also called boosting techniques, random forests feature a group of decision trees positioned together. Using this technique, machines notice when multiple weak signs of fraud occur, and although they typically overlook these fraud indicators, they learn when the confluence of these weak signs indicate an increased potential for fraud.
This type of machine learning is also called boosting because it helps the machines learn when to pay attention or when to “boost” awareness of traditionally insignificant signs of fraud.
Designed to simulate the human brain, neural networks learn patterns, and they are the backbone of deep learning algorithms. They start by generating outputs for basic problems based on simple inputs.
Then, they assign weights to each of the inputs to determine their importance, and they build in strategies to account for discrepancies based on inherent bias. Finally, these basic formulas are knit together into a network, and if there are more than three layers deep, they are considered to be deep learning algorithms.
Unsupervised learning, in contrast, involves inferring structures from unlabeled data sets. For example, a fraud detection tool will look at thousands of bank transactions. It will assume that the majority of the transactions are legitimate, and without any inputs from humans, it will identify the patterns and anomalies that signify the fraudulent transactions in the data set.
K-means clustering puts data sets into clusters. The machine sorts data points into a predefined number of categories based on their similarities. This categorization process allows the machine to assume that data that doesn’t not fit into the most common categories is likely to be fraud.
Local Outlier Factor
This type of machine learning looks for data outliers. It groups similar activities together based on the similarities in their data density points, and then, it labels the outliers as potentially fraudulent.
One-class support vector machine uses labeled data sets to classify transactions into one of two groups, and in the case of fraud detection, the two groups are “fraudulent” or “legitimate”. SVM finds a hyperplane that allows the machine to distinguish characteristics of each of the classes and label transactions accordingly. One-class SVM works well in situations where there are very few examples of the minority (less common) class — in this case, the fraudulent class.
Machine Learning Is Constantly Improving
With all of these algorithms, the machine is constantly learning. The algorithms don’t create a static rule set. Instead, they allow the machine to take a dynamic approach to fraud detection, and every day, the machine learns more about how to improve its ability to spot fraud and reduce false positives.
Contact SQN to Talk About Machine Learning Anti-Fraud Tools
At SQN Banking Systems, we use a variety of machine learning algorithms and techniques in our fraud detection and prevention solutions. To learn more about protecting your financial institution with machine learning and artificial intelligence, contact us today.
We can help you implement real-time fraud detection and prevention solutions that are constantly learning and refining their approach to fraud detection.