Fraud is a growing problem, costing businesses and consumers billions of dollars yearly. While traditional rule-based systems can detect some fraud, machine learning algorithms offer a powerful new approach to catch even the most sophisticated fraudsters.
In this post, we will explore five latest algorithms of machine learning for fraud detection. From neural networks to gradient boosting machines, these innovative techniques work on large datasets and advanced mathematics to sniff out anomalies and recognize suspicious patterns that humans could never detect on their own.
With fraud becoming ever more prevalent, companies must stay on top of the newest technology to protect their bottom line and customers. Read on to learn about these five machine learning algorithms leading the charge in the ongoing battle against fraud.
Latest Fraud Detection Machine Learning Algorithms
Machine Learning is advancing continuously with new research and methodologies. The 5 new fraud detection machine learning algorithms include:
- Graph neural networks (GNNs)
- Adversarial learning
- Federated learning
- Explainable AI (XAI)
- Reinforcement learning (RL)
Let’s discuss how each of them works and facilitates fraud detection.
1. Graph neural networks (GNNs)
GNNs are machine learning algorithms that can be used to analyze data represented as a graph. These are a type of neural network designed to work with graph-structured data. In fraud detection, GNNs can analyze the relationships between different entities like users, devices, and transactions to identify patterns of fraudulent activity.
GNNs operate by passing messages between the nodes of a graph. Each node represents an entity, and the edges represent relationships between the entities. By analyzing the graph structure, GNNs can detect clusters of related fraudulent activity or anomalies in the relationship patterns.
How it works?
For example, a GNN could analyze the network of transactions between users to identify mule accounts used for money laundering. The model would look at each user node and its edges connecting it to transaction nodes. By learning the patterns of activity in legitimate versus fraudulent accounts, the GNN can flag when new transactions fit the profile of money laundering or other financial crimes.
Example
One real-world example is the Felnett system developed by ComplyAdvantage. It uses GNNs to model the relationships between entities and uncover complex schemes like invoice fraud, spoofing, and more.
By understanding connections in the network, Felnett can identify risky transactions that may appear normal when looked at in isolation. This showcases the ability of GNNs to detect sophisticated fraud that rules-based systems would miss.
2. Adversarial Learning
Adversarial learning is a machine learning fraud detection technique where two models are pitted against each other in an adversarial setting.
How it works?
In adversarial learning, one model (the generator) tries to produce fraudulent data that can fool the second model (the discriminator), whose job is to distinguish between legitimate and fraudulent data. The two models are trained together in an iterative process that improves both over time.
For fraud detection, the generator learns to make increasingly realistic examples of fraudulent transactions, accounts, etc. As it improves, it forces the discriminator model to become better at telling the difference between the generated fraud and real legitimate data.
This “adversarial arms race” produces a discriminator that excels at identifying even artfully disguised instances of fraud. It is able to flag subtle patterns in data that would be difficult to detect with rules alone.
An adversarial learning system could generate fake credit card transactions that mimic real spending patterns. The discrimination model learns to classify these as fraudulent despite their realism. This allows the model to detect identity theft or fraudulent transactions that criminals intentionally try to make look legitimate.
Example:
One company utilizing adversarial learning for fraud is DataVisor. Their unsupervised models utilize generative adversarial networks (GANs) to model normal user behavior and transactions. By constantly challenging the system, they can detect increasingly sophisticated fraud attacks.
3. Federated Learning
Federated learning is a privacy-preserving machine learning approach that can be highly useful for fraud detection.
In federated learning, the training data remains decentralized on each user’s device rather than uploaded to a centralized server. The model is trained collectively across all the devices by aggregating and sharing only the model updates rather than raw data.
This allows for collaborative modeling without compromising user privacy. Banks or financial service providers could leverage their customers’ data to improve fraud detection accuracy without accessing sensitive transaction information.
How it works?
A credit card company could deploy a federated learning model across all of its users’ devices. Locally on each device, the model could analyze purchase history and develop patterns to distinguish legitimate vs fraudulent transactions for that specific user.
Only the patterns are shared, not the actual transaction data. The centralized model aggregates these updates to build a robust global fraud detector personalized to each individual user.
Example
One real-world example is a system developed by Mastercard and Data61. They use federated learning to collaboratively train a model for identifying credit card fraud across banks without exposing customer data. This allows the detection of new fraud methods by sharing insights across institutions in a privacy-preserving manner.
The decentralized nature of federated learning makes it highly promising for fraud detection cases that require strong privacy protections.
Explainable AI (XAI)
Explainable AI (XAI) refers to machine learning techniques that can produce models whose internal reasoning processes and predictions are understandable and transparent to humans. This interpretability is crucial for fraud detection.
Fraud investigators need to know why a machine learning model flagged a transaction or account as fraudulent in order to take appropriate action. XAI methods can provide this insight into the model’s logic.
How it works?
Techniques like LIME (Local Interpretable Model-Agnostic Explanations) can indicate the most influential factors behind an individual prediction. This highlights why the model classified a specific transaction as fraudulent to inform further investigation.
Other XAI approaches, like counterfactual explanations, can show how the prediction would change if certain data features were different. This reveals what data patterns the algorithm relies on most when evaluating transactions.
For fraud analysts to validate the model’s reasoning or identify potential errors and biases, XAI is essential. Domain experts can only trust and successfully apply a model if they understand how it arrives at its conclusions.
Example:
FinCEN AI from NICE Actimize provides XAI functionality like LIME to interpret their detection models. It enables compliance teams to unpack risk predictions and make informed decisions. Without XAI, they would have limited visibility into why the AI flagged certain activity as fraudulent or risky.
Reinforcement learning (RL)
It is a machine-learning approach based on rewarding desired behaviors and outcomes. It can teach fraud detection systems to take optimal actions in different situations.
How it works?
In RL, an agent learns through trial-and-error interactions with an environment. When the agent takes a good action, it receives a reward to reinforce that behavior. Over time, the agent learns to maximize rewards through its actions.
For fraud detection, RL can optimize policies for reviewing and investigating alerts. The system learns effective strategies for allocating human resources or prioritizing the most suspicious activities.
For example, the RL agent may learn that reviewing accounts with a sudden spike in activity leads to more confirmed fraud cases vs chasing every minor alert. RL reduces false positives and improves fraud coverage by optimizing the review process.
Example
One real-world implementation is Feedzai’s system using RL for fraud management operations. The AI agents choose actions like alert escalation and case prioritization to maximize fraud detection and minimize unwanted alerts. This reduces human effort in low-risk cases.
Over time, the RL agents learn nuanced strategies superior to rigid rules. They adapt to new fraud tactics and allocation constraints to best use limited resources. This showcases RL’s ability to optimize fraud operations.
Summary
The machine learning algorithms we’ve explored demonstrate the huge potential of AI for fraud detection. As fraudsters come up with new ways to exploit systems, machine learning provides the adaptability to stay ahead of these evolving threats.
While rules-based approaches can address known fraud patterns, machine learning offers the ability to uncover never-before-seen attacks. By building sophisticated models on large datasets, algorithms can identify complex anomalies and inter-relationships indicative of fraud.
These are just a few new machine learning algorithms being developed for fraud detection. As the field of machine learning continues to evolve, we can expect to see even more innovative approaches to detecting and preventing fraud.
Ready to leverage AI to combat fraud? VisionX provides cutting-edge machine learning development services tailored for fraud detection use cases. Our data scientists and machine learning engineers can build, deploy, and iterate custom AI solutions to meet your organization’s needs.
Get in touch today for a consultation on putting the power of machine learning to work, detecting and preventing fraud.
