While machine learning may seem incomprehensible, it is much easier to understand than you might think. In our previous article ‘All You Need to Know About Machine Learning Based Fraud Detection Systems‘ we talked about machine learning vs. rule-based systems in fraud detection and the benefits of using machine learning in fraud detection. In this article, we will take a look at what goes into building a machine learning-based system and the most common types of ML models that are used in payment fraud detection.
Creating a Machine Learning Payment Fraud Model
Dataset Preparation
Before you can do anything else, you need to prepare a dataset. In most cases, the data points you use will need to be manually labelled as either genuine or fraudulent.
Your archive of past payments, which has already been labelled by your security team, will make for a perfect dataset.
The bigger (and higher quality) the pool of data you will train your neural network on, the more accurate and efficient your system will be.
Introduction of Features
Next, you will need to introduce features, which are data points describing customer behavior and giving you a clear signal that something is wrong with the transaction.
The most common features in payment processing are:
- Customer identity
- Order information
- Payment method
- Location information
Having a large, pre-prepared corpus of fraudulent payment features will help your system identify fraudulent payments with ease from the very start.
Algorithm Training
After features are introduced, you need to train the algorithm on a training set of historical data. Once the training phase is complete, you will have a finished model that can start identifying fraudulent payments.
The higher quality the training set, the better and more accurate the system will be.
Continuous Improvement
During the first stage of its operation, your security team will need to monitor the system and make sure that it is performing the way it should.
A great security team will also be able to log all of the algorithms’ mistakes and errors. These will be labelled and added to the dataset that will be used to train a new version of the model.
As a result of these actions, the system will become better and better as time goes on.
As you can see, training up an ML algorithm is by no means easy and can be very expensive in terms of both man-hours and funds. Thankfully, third-party solutions exist to help businesses take advantage of the latest developments in AI technologies at a minimal cost.
Top 5 Most Popular Machine Learning Models
The five most popular ML models are random forest, support vector machine, k-nearest neighbors, neural networks, and deep neural networks. Let’s take a bird’s-eye view at them all.
Random forest
One of the best examples of intuitive naming in machine learning, a random forest is essentially a collection of separate decision trees that are “grown” using the training set.
When a piece of data needs to be classified, each of the decision trees will tell the forest how close it is to its class. The forest then picks the tree that gave the new piece of data the most votes.
In the context of payment fraud, you can create a forest filled with various common and not so common types of transactions. When a new transaction occurs, the random forest will immediately tell your team what type of transaction it is.
Support vector machine
Another popular classification method is the support vector machine (SVM). In this method, each feature is presented as a coordinate point. Each data item is plotted as a point on an axis of features.
If we wanted to create a classification of all transactions based on two variables, such as account age and payment sum, we’d plot the two variables in a 2D space where each piece of data would have two coordinates.
Large payments from a new account would be labeled as high-risk. Smaller payments from older accounts would be labeled as being safer.
K-nearest neighbors
Another simple, yet effective algorithm is K-nearest neighbors. It stores all available cases and then classifies all new cases via a majority vote from its K neighbors. The case assigned to a new class will be the most common among its K nearest neighbors as measured by a distance function.
This system is very similar to the way we intuitively classify things as people. Let’s say your bank gets a new corporate client. This client is a member of various industry organizations, has accounts in a wide variety of other banks, and regularly does business with many of your best clients. Birds of a feather flock together. You and your security team instinctively know that this client is legitimate, because their “neighbors” are trustworthy.
Neural networks
Neural networks are based on a model of the human brain. They are designed to recognize patterns in raw data. Thanks to this, they can help businesses classify and cluster vast amounts of information quickly and efficiently.
Simply supply a labeled data set to a neural network and it will be able to group future data according to similarities without the need for you to add any additional features (although features can still be used to reinforce the model).
Deep neural networks
White normal neural networks are great for a lot of classifying work, they can never be creative. That’s where deep neural networks come in.
Being a much more complicated and many-layered system than regular neural networks, deep neural networks can also do a lot more. Companies use them for such things as analytics, predicting future outcomes, solving creative-thinking tasks, and even creating art.
What’s more, deep neural networks do not need as much guidance as their regular neural networks and can even function with completely unlabeled data sets. Which means that you can use deep neutral networks to solve problems you yourself don’t know how to solve.
Examples of deep neural networks include the Deep Dream Generator, YouTube and Tik-Tok content-serving algorithms, as well as Sony CSL’s music-creation algorithm.
Daddy’s Car, a Beatles-style song generated by the Sony CSL AI
Deep learning fraud prevention systems are still somewhat rare, but the technology has a lot of potential to revolutionize the way financial systems work and create a much safer operating environment for financial institutions and their clients.
Watch the SDK.finance’s demo video to explore how SDK.finance provides a comprehensive view and control over client transactions, along with advanced AML and fraud prevention features, empowering institutions to stay ahead in the fight against financial crime:
Final Words
Machine learning-based fraud prevention is an exciting new development in the prevention of illicit payments.
By replacing outdated rule-based systems with modern machine learning solutions, banks and payment processors can reduce the losses they incur due to fraud, lower their security system-related expenses, and reduce payment friction for their clients.
As for the companies that are hesitant to switch, the costs associated with maintaining their legacy payment fraud systems will eventually outweigh the investment necessary to introduce the more modern system. It is predicted that all major financial industry players will eventually transition to machine learning-based payment fraud prevention systems.
