How American Express Uses Deep Learning for Better Decision Making

by John Ashley

Financial fraud is on the rise. As the number of global transactions increase and digital technology advances, the complexity and frequency of fraudulent schemes are keeping pace.

Security company McAfee estimated in a 2018 report that cybercrime annually costs the global economy some $600 billion, or 0.8 percent of global gross domestic product.

One of the most prevalent — and preventable — types of cybercrime is credit card fraud, which is exacerbated by the growth in online transactions.

That’s why American Express, a global financial services company, is developing deep learning generative and sequential models to prevent fraudulent transactions.

“The most strategically important use case for us is transactional fraud detection,” said Dmitry Efimov, vice president of machine learning research at American Express. “Developing techniques that more accurately identify and decline fraudulent purchase attempts helps us protect our customers and our merchants.”

Cashing into Big Data

The company’s effort spanned several teams that conducted research on using generative adversarial networks, or GANs, to create synthetic data based on sparsely populated segments.

In most financial fraud use cases, machine learning systems are built on historical transactional data. The systems use deep learning models to scan incoming payments in real time, identify patterns associated with fraudulent transactions and then flag anomalies.

In some instances, like new product launches, GANs can produce additional data to help train and develop more accurate deep learning models.

Given its global integrated network with tens of millions of customers and merchants, American Express deals with massive volumes of structured and unstructured data sets.

Using several hundred data features, including the time stamps for transactional data, the American Express teams found that sequential deep learning techniques, such as long short-term memory and temporal convolutional networks, can be adapted for transaction data to produce superior results compared to classical machine learning approaches.

The results have paid dividends.

“These techniques have a substantial impact on the customer experience, allowing American Express to improve speed of detection and prevent losses by automating the decision-making process,” Efimov said.

Closing the Deal with NVIDIA GPUs 

Due to the huge amount of customer and merchant data American Express works with, they selected NVIDIA DGX-1 systems, which contain eight NVIDIA V100 Tensor Core GPUs, to build models with both TensorFlow and PyTorch software.

Its NVIDIA GPU-powered machine learning techniques are also used to forecast customer default rates and to assign credit limits.

“For our production environment, speed is extremely important with decisions made in a matter of milliseconds, so the best solution to use are NVIDIA GPUs,” said Efimov.

As the systems go into production in the next year, the teams plan on using the NVIDIA TensorRT platform for high-performance deep learning inference to deploy the models in real time, which will help improve American Express’ fraud and credit loss rates.

Efimov will be presenting his team’s work at the GPU Technology Conference in San Jose in March. To learn more about credit risk management use cases from American Express, register for GTC, the premier AI conference for insights, training and direct access to experts on the key topics in computing across industries.