How to Improve Fraud Detection With Machine Learning

If your business handles any transactions or sensitive data online, effective fraud detection is critical. You have likely wondered about the most efficient way to identify and prevent fraudulent activity without inconveniencing your genuine customers.

Traditional methods (such as WAFs and siloed CAPTCHAs) are no longer effective against today’s advance threats. Thankfully, fraud detection software continues to evolve alongside advances in artificial intelligence (AI), data science, and machine learning (ML).

ML fraud detection techniques play a big part in mitigating losses and preventing catastrophe for businesses, both financially and operationally. Static defenses can only carry out specifically programmed rules—but dynamic learning systems can respond to the changing threat landscape in real time, making ML foundational to any effective solution for ongoing fraud protection.

But how does fraud detection machine learning work, and what can it do for your business? Let’s take a look.

What is fraud detection machine learning?

Machine learning is increasingly used in fraud detection for e-commerce businesses, governments, apps, and online services to detect and prevent sophisticated, often automated attacks that threaten to damage your infrastructure and steal your data, goods, and funds.

For fraud detection, machine learning models must be trained using historical data about fraud (attack attempts, sources, methods, etc.). ML algorithms can be used to recognize patterns in a historical dataset, and then dynamically change a solution’s security rules to prevent future fraud attempts—even attempts using methods that have never been seen before.

Machine learning in the fraud detection context is a smart adaptation that is now necessary in today’s volatile cybersecurity environment. ML detection is much more effective than human intervention, which requires people to manually looking for patterns and creating rules to try and mitigate specific threats. ML is the best response to the evolving nature of online threats, giving users a massive advantage in the fight against card fraud, fake account creation, account takeovers (ATOs), and credential stuffing.

How do machine learning and AI technology differ?

ML and AI are linked, but there are some key differences. Essentially, machine learning is a subset of AI (artificial intelligence).

AI generally refers to computer systems that mimic human thought or behavior in some way, such as creative idea generation or problem-solving. Most tech products that claim to be AI-powered use the term “AI” because the system is intelligent enough to identify and carry out what it need to do under certain circumstances. Automation of tasks is a common AI feature.

Machine learning is a particular application of artificial intelligence that allows a computer to automatically learn from past data without explicit instruction. For example, DataDome’s AI for fraud detection monitors live, incoming data and reacts responsively. But first, the solution decides what the AI triggers are by using machine learning to look at historical data.

Machine Learning vs. Old-School Fraud Detection

Online fraud is constantly evolving, and malicious actors use an arsenal of automated tools (including AI) to develop new attack methods. Armies of bots can be assembled in minutes to launch a new offensive.

Traditional fraud detection systems have serious limitations:

• First, they’re based on static rules. While the rules may work great initially, they become less useful over time as technology evolves and attack methods change. Bad actors want to achieve their goals with as little effort as possible, so they won’t waste resources trying the same approach that doesn’t work again and again—they will find a way around the static barrier.
• Traditional systems rely heavily on human labor, so they are limited by the expertise, time, and energy of the people who create and manage their rules. Manually operated systems can eventually become so complex that it is nearly impossible for new users to understand how to manage them.

Machine learning for fraud detection solves these issues. ML is faster, more accurate, and more cost-effective, eliminating the need for a human to supervise every decision, processing new data automatically, and updating detection models in real-time.

And unlike the human brain, the more data you feed a machine learning algorithm, the better and more accurate it becomes.

That said, ML is not always perfect.

Drawbacks of ML for fraud detection include the potential for false positives, describing when a system mistakenly marks legitimate actions as fraudulent. False positives open up the possibility for a negative feedback loop—if one detection mistake isn’t spotted, the algorithm thinks it responded correctly and the behavior was legitimate, teaching itself to repeat the same response in the future.

Thankfully, human insight alongside machine learning can help overcome this problem, which is why we recommend 24/7 expert monitoring of all fraud detection machine learning models.

3 Major Benefits of Machine Learning for Fraud Detection

Using machine learning models for fraud detection (instead of manual supervision) is a power move for businesses thanks to three major benefits:

1. Cost-Effectiveness: By automating fraud detection and leveraging machine learning, you reduce your costs associated with manual fraud detection, including the cost of labor, technology, and time. This allows you to allocate resources more efficiently and reduce your overall fraud-fighting expenses.
2. Accuracy: Machine learning algorithms are trained on big data volumes to identify patterns and anomalies that humans simply cannot be expected to catch (and at speeds humans are incapable of). Therefore, monitored ML can significantly reduce your number of false positives and false negatives (key indicators of detection accuracy) compared to traditional, manual methods.
3. Relentlessness: While humans can only analyze data for a limited number of hours each day, machines can do it 24/7, without getting burnt out or overloaded. In fact, the larger the amount of data processed, the better an ML algorithm usually performs.

Using Human Insights to Enrich Machine Learning

While fraud detection machine learning is a really powerful tool, a true fraud protection solution must optimize the ML models with 24/7 monitoring by human experts.

While ML models can perform really well on autopilot (when left completely alone to carry out pre-programmed rules), there are unique cases in which the ML can falsely mark normal behavior as problematic/potentially fraudulent. For example, an unusually high-value transaction could indicate stolen card details, or it could just be a regular customer making a large purchase.

It’s difficult for a machine to understand the nuances of human psychology and behavior. ML models cannot put themselves in the shoes of a human (whether it’s a customer or an attacker) or use deductive reasoning to figure out why and how a user might do something.

A comprehensive and adaptive solution must be taught when an initial response is not right, and intervention from a threat expert is the best way to correct and train the solution. So, your best bet is a solution that combines the power of the two:

1. A solution that uses powerful machine learning to sift through trillions of data signals and respond accordingly.
2. Human experts with extensive domain experience and the ability to think like an attacker.

How does fraud detection with machine learning work?

There are four main stages to the process of building machine learning for fraud detection:

1) Data Collection

We start by feeding the data into the system. The ability of the system to correctly identify threats is decided by data quality—accurate detection requires good, relevant data. The phrase “garbage in, garbage out” applies. And in the case of machine learning, more data is generally better. But it must be curated and specific to the business in question.

Therefore, relevant data is segmented and extracted from the dataset under instruction from the user.

2) Data Extraction

Next, we decide what data is relevant, based on the features most important to the task at hand, and extract the relevant data from the dataset. 

For example, if you are in need of e-commerce online fraud detection, we might focus on your transaction data, the way your website is interacted with, or how connections are made with your service (devices, IPs, proxies, etc.). With these categories, we would specify which behaviors are suspicious and likely to be fraudulent.

3) Model Creation

Based on the relevant features, we can deploy an algorithm on training data to get it working. The algorithm is a set of decision-making rules that will judge the nature of interactions and whether they are fraudulent or not. The exact way the decision-making process works is decided by the type of algorithm we use (we’ll go through some examples below).

You end up with a predictive machine learning model created to identify potentially shady future activities with a high level of confidence.

4) Model Testing

Finally, the ML model is tested in a sandbox environment. Before letting it loose on live information, we test models on real historical data to see how well they perform.

Our experts continue to monitor and tweak rules based on how well each model is doing, and how accurate its assertions are. The SOC team can identify any potential issues or false positives before releasing a model into a live environment.

Common Fraud Detection Algorithms

Depending on the type of data being analyzed and the desired result, different algorithms can be used. They come in different types:

• Supervised Learning: A commonly-used type of algorithm that uses labeled data to learn patterns and make predictions. The training data will have been manually classified as potentially fraudulent or not, so the system can understand the different characteristics and monitor for them. This involves an up-front human setup to get things in motion, and relies upon having a good-quality historical dataset.
• Unsupervised Learning: Used when there’s not much historical data to go on, so the system has to figure out patterns from new data coming in and decide what’s fraudulent and what is legitimate. The system will look for potential anomalies in new activity and flag them as problematic. Also known as “clustering”, this technique spots unusual behavior and highlights it for inspection, constantly monitoring and updating its own ruleset as it learns through experience.
• Semi-Supervised Learning: Used when labeling all your data is either impossible or too expensive, this combines supervised and unsupervised learning techniques to get the best of both. Here, human experts are needed to label portions of the data. This can work in fraud detection applications by using labeled data for training and unlabeled data for validation.
• Reinforcement Learning: An algorithm that learns from mistakes using trial-and-error techniques to find the best solution in a given environment. By performing different actions over and over, the system learns what the optimal behavior is. For it to learn, it needs to get feedback, like a reward or punishment, for each action it takes. Feedback helps the algorithm figure out which actions are good and which are bad, eventually finding the best actions that reduce risks and increase rewards.

Here are some of the actual algorithms used in fraud prevention scenarios:

• Logistic Regression: This statistical model is used to predict the probability of a binary outcome, such as either “fraud” or “not fraud”.
• Decision Tree: Makes decisions by breaking down complex problems into smaller, simpler parts. Branches of the tree represent possible outcomes, which are analyzed to identify patterns indicating fraudulent behavior.
• Random Forest: A combination of multiple decision trees, each trained on a different subset of data, outputting the average prediction of them all. Random forests can handle nonlinear relationships between data features, which are important in detecting complex fraud patterns.
• Neural Networks: This type of machine learning algorithm is modeled after the structure of the human brain, using a layer-based “deep learning” approach to pattern recognition. A neural network can be used in combination with other machine learning algorithms to enhance the performance of your fraud detection system.

Other algorithms sometimes used in fraud detection ML are Support Vector Machines, K-Nearest Neighbors, and Naive Bayesian.

Ways Businesses Can Use Machine Learning for Fraud Prevention

Machine learning helps enterprises prevent fraud and protect against financial losses by analyzing large amounts of activity data. Here are some examples of how ML techniques are being used to fight the multitude of ongoing threats:

1) Credit Card Fraud Detection

Credit card companies and card payment gateways can use ML systems to analyze transaction data and user behavior to figure out when fraudsters are attempting to get around their safeguards. Carding and card cracking are commonly-automated fraud mechanisms that rely on using bots to test the validity of stolen data. Machine learning algorithms can figure out how to identify and put a stop to card cracking before it negatively impacts your data and system integrity.

2) Fraud Detection in Banking

Banks are also using machine learning methods to scan through customer transactions to identify patterns that might indicate suspicious activity.

ML methods are more agile and responsive than using static rules, like flagging transactions over a certain value as suspicious. ML also doesn’t require massive resources or slow down genuine transactions, harming your legitimate customer experience.

ML detection in banking can be used at multiple scales—to spot single instances of potential fraud transactions or to uncover enterprise-level financial crime operations.

3) Account Takeover Fraud Detection

Online businesses use ML algorithms to analyze customer data, such as shipping information, payment methods, and IP addresses, to identify patterns that might be indicative of fraud.

Account takeover (ATO) attacks are a common threat in which fraudsters gain access to user accounts and use them for malicious activities, like identity theft or transferring funds. ML can be used to detect ATO attempts by analyzing customer behavior patterns, such as sudden changes in purchase amount or login location, and flagging suspicious activity. ML can allow online enterprises to detect illicit transactions in real time and prevent financial losses.

Using Machine Learning at the Edge for Fraud Detection & Prevention in Real Time

At DataDome, machine learning is a core element of our solution, helping us protect online enterprises against fraudsters, malicious bots, and all types of attacks.

Our cloud-based cybersecurity platform provides protection against online fraud and automated threats for websites, mobile apps, and APIs, as a key player in fraud prevention for online businesses across the world.

FAQs

1. How can machine learning detect fraud?
   Machine learning can detect fraud by analyzing huge amounts of raw data under specific instruction to identify patterns and anomalies that indicate suspicious user behavior. It can be human-monitored for more accurate results without false positives.
2. What is the best machine learning algorithm for fraud detection?
   It depends on the volume and complexity of the data, the type of fraud that you’re looking for, and the level of accuracy and speed you’re willing to accept. The best algorithm will require little manual intervention when it’s up and running.
3. What is ML-based financial fraud detection?
   ML-based financial fraud detection is a technology that uses algorithms and statistical models to identify fraudulent transactions in financial accounts. Scanning through large amounts of data, ML algorithms identify problematic actions and either block or highlight them for review.