Featured Blog Post:
In the world of identity management and security, fraudsters are constantly evolving their tactics, exploiting weaknesses in outdated device identification methods. For fraud, identity and digital experience teams, securing...
Two prominent and often-discussed areas are generative AI and AI used in fraud prevention systems (predictive AI). Despite both leveraging advanced machine learning models, their algorithms are tailored to their specific goals—generating realistic content versus detecting and preventing fraudulent activities. The choice of models, learning methods, and evaluation criteria reflects these distinct objectives.
Predictive AI and generative AI serve different purposes within the realm of artificial intelligence. Predictive AI focuses on forecasting future events or outcomes based on historical data, using models to identify patterns and make predictions, such as detecting fraud or predicting customer behavior. It relies heavily on supervised learning techniques where the goal is to minimize the difference between predicted and actual outcomes. In contrast, generative AI is designed to create new, realistic data samples by learning the underlying distribution of a given dataset. It often employs unsupervised or semi-supervised learning methods, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to generate content like images, text, or music. While predictive AI aims to make accurate predictions, generative AI aims to produce new data that mimics the characteristics of the training data.
Let’s review the differences in the context of supervised vs unsupervised learning.
Supervised learning is a type of machine learning where a model is trained on labeled data. This means that for each training example, the model is provided with input data and the corresponding correct output, often referred to as the label. The goal of supervised learning is to learn a mapping from inputs to outputs that can be applied to new, unseen data.
In the context of fraud prevention, supervised learning is used to train models that can identify fraudulent transactions or activities by learning from historical data where each instance is labeled as either fraudulent or non-fraudulent. The goal is to build a model that can accurately classify new, unseen transactions as either legitimate or fraudulent based on patterns learned during training. Algorithms such as decision trees, random forests, and neural networks are commonly used. While decision trees and random forests are supervised models, neural networks can be used in both supervised and unsupervised learning contexts, as well as in semi-supervised and reinforcement learning scenarios.
Decision trees and random forests are generally not used in generative AI. Generative AI typically requires models that can learn complex, high-dimensional data distributions and generate new data that resembles the training data. Decision trees and random forests are more suited for tasks like classification and regression, where the goal is to make decisions or predictions based on input features.
While both generative AI and predictive AI leverage supervised neural networks, they do so in distinct ways tailored to their specific tasks. predictive AI for fraud prevention focuses on identifying and classifying fraudulent activities, often employing feedforward networks, RNNs, and LSTMs.
Feedforward networks are used to classify transactions as either fraudulent or legitimate based on various input features. These features might include transaction amount, time, location, user behavior patterns, and other relevant data. Feedforward networks are generally not the primary choice for generative AI tasks.
Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) are used for fraud prevention particularly when dealing with sequential data. Their ability to model temporal dependencies and sequential patterns makes them well-suited for detecting fraudulent activities over time. An example is analyzing a user’s transaction history over time to detect anomalies such as sudden large transactions after a series of small transactions. RNNs and LSTMs are commonly used in Generative AI as well for text generation, music composition, and other tasks that involve creating sequential data.
Unsupervised learning refers to a type of machine learning where the model is trained on data without labeled responses. Unlike supervised learning, where the model learns from input-output pairs (where the output is known and provided), unsupervised learning involves finding patterns, structures, or relationships within the input data itself.
Unsupervised models are essential for fraud prevention, particularly when labeled data is scarce or unavailable. These models can detect anomalies and unusual patterns that may indicate fraud by learning the normal behavior of transactions and flagging deviations. Common unsupervised models include autoencoders, clustering algorithms, isolation forests, PCA, one-class SVMs, Gaussian mixture models, and self-organizing maps. Each technique offers unique advantages for identifying potential fraudulent activities in a variety of data types and structures.
While clustering and standard statistical methods are not typically generative in nature, they play supportive roles in the development, training, and refinement of generative models. Autoencoders, particularly VAEs, are directly used as generative models, leveraging their ability to learn and sample from latent space representations.
GANs consist of two neural networks, a generator and a discriminator, trained together in a competitive framework. The generator creates data samples, while the discriminator evaluates them against real data. GANs are widely used for generating realistic images, videos, and other media content. They are known for their ability to produce high-quality synthetic data.
The focus of GANs on generating new, synthetic data does not align with the primary goal of fraud prevention, which is to identify and mitigate fraudulent activities based on existing data patterns. However, GANs can still be applied in fraud prevention in a few innovative ways, particularly for generating synthetic data and enhancing anomaly detection models. For example, GANs can generate realistic synthetic transaction data that mimics the distribution of real data. This can be useful for training and testing fraud detection models, especially when labeled fraudulent data is scarce. Overall, while GANs are primarily designed for generative tasks, their application in fraud prevention is possible but comes with certain challenges.
While both generative AI and predictive AI for fraud prevention harness the power of advanced machine learning, their applications, methods, and goals set them apart. Generative AI opens new frontiers in creativity and simulation, while fraud prevention AI safeguards transactions and activities across various sectors. Understanding these differences is crucial for leveraging AI effectively in diverse contexts, whether you’re aiming to innovate or protect.
