Fake It to Make It: Companies Beef Up AI Models With Synthetic Data

Companies rely on real-world data to train artificial-intelligence models that can identify anomalies, make predictions and generate insights. To detect credit-card fraud, for example, researchers train AI models to look for specific patterns of known suspicious behavior, gleaned from troves of data. But unique, or rare, types of fraud are difficult to detect when there isn't enough data to support the algorithm's training. To get around that, companies are learning to fake it, building so-called synthetic data sets designed to augment training data. At American Express Co., machine-learning and data scientists have been experimenting with synthetic data for nearly two years in hopes of improving the company's AI-based fraud-detection models, said Dmitry Efimov, head of the company's Machine Learning Center of Excellence. The credit-card company uses an advanced form of AI to generate fake fraud patterns aimed at bolstering the real training data.