How anonymized and aggregated transaction data powers new AI models

Imagine if you could leverage the business intelligence of one of the largest transaction data sets in the world. Trained on Mastercard’s anonymized and aggregated global transaction data, Brighterion’s new market-ready artificial intelligence (AI) models are revolutionizing transaction fraud prevention.

Financial institutions (FIs) face fraud and credit risks every day. Nine out of 10 acquiring banks reported transaction fraud increased during COVID-19. Meanwhile, U.S. lenders are doing business amidst rising interest rates – with household debt at an all-time high of $17.06 trillion. Now these problems can be managed quickly and accurately with out-of-the-box AI solutions that are ready to deploy globally in as little as 30 days. The risk of more time-consuming custom model building has been removed and the results speak for themselves.

With Mastercard’s global network of 210 countries and territories, the breadth of transaction data is vast. Using transaction data* for financial data analytics while respecting customer privacy has become a core competency for Mastercard. There are no identifying factors in the financial data; account information, geography, merchants and other identifiers have been stripped out. FIs can benefit from models trained on this data, have learned patterns and anomalies and can instantly score transactions to deliver intelligence in real time.

Turning AI expertise and transaction data* experience into the future of AI for fraud and risk

Brighterion AI has proven its accuracy in preventing transaction fraud on the Mastercard network. With over 20 years’ experience in AI innovation, Brighterion strategized a way to develop faster-to-implement models. These models leverage Mastercard’s 55+ years of payments experience and use current anonymized and aggregated data to enrich the models.

Pre-trained AI models don’t have to refer to network data during transactions or to score events. This enables the cloud-native platform to deliver a remarkably low latency of 100-120ms and, when deployed on-premises, a speed of less than 10ms.

Global transaction data* helps acquirers detect fraud faster

These more timely, broadly trained models are beneficial to acquirer, payment processors and payment facilitators. Both transaction fraud and merchant risk increase with economic pressures, exposing acquirers to potential losses.

Global transaction data* brings knowledge from the broader market to deliver fewer false positives and higher fraud detection rates. Using Brighterion’s market-ready AI, one acquirer saw an increase in fraud detection of two to three times and an increase in approval rates of 7.4 percent.

Acquirers can monitor merchants for viability and risk while reducing friction for trusted users. The models provide a 360-degree view of the acquirer’s merchant ecosystem, covering risks such as fraud and collusion.  

How does training AI on Mastercard transaction data* differ from using an FI’s own historical data?

Building traditional/custom AI models

Traditional, custom AI models require a longer turnaround time. Some FIs struggle to extract the right financial data for model building and training or may not have the historical data that developers need.

FIs are burdened with extracting huge datasets to meet this need, ensuring correct labeling and effective data transfer. The required datasets may be in the hundreds, requiring hours of work. These are used to train the new model to identify anomalies relevant to the business’s specific challenges.

Initializing self-learning, market-ready AI models

Market-ready models are built with a variety of advanced AI and ML technologies. These turnkey solutions move beyond the intelligence found in an FI’s historical data; they are trained with the business intelligence derived from more than 150 billion transactions a year processed by Mastercard. By using this more robust data set, the model has expanded knowledge.

The key advantage of market-ready AI is that it saves FIs time and resources as the model is already built and trained, reflecting high accuracy rates. The model is ready to deploy with a small sampling of the FI’s transaction data. During this period, the customizable API interface is fine-tuned to the customer’s needs. Scores are then returned to the FI and the model is ready for full deployment.

Market models for acquirers are brand-agnostic. It is the scope of business intelligence that FIs benefit from, not the network brand involved. As with Brighterion’s custom AI models, market-ready models are self-learning. They continue to learn and evolve with each new transaction or output with oversight by Brighterion computer scientists.

Save money by blocking fraud sooner

Acquirers save money by deploying transaction data solutions early in the pre-authorization stage rather than at the transaction stage. By blocking fraud before it hits issuer or network solutions, acquirers are:

  •     Clarifying where the fraud liability lies, whether the issuer, network or acquirer

  •     Saving money that would be lost to transaction costs, merchant fees and chargebacks

  •     Increasing accuracy of prediction rates

  •     Decreasing fraud rates to protect accuracy ratings

  •     Preventing the most difficult to catch fraud that would have been cleared at other stages of the transaction process

  •     Reporting a 15 to 30 percent lift in detection rates

A place for custom AI

Brighterion has the process of building custom models down to a science – preparing customers for deployment in a couple of months with its AI Express implementation process.

Custom built AI models don’t have to be complicated and are still the right fit for specific or unique business challenges where innovation and experimentation are needed.

Do market ready models spell the end of custom AI? No, but for certain use cases, market-ready AI can save FIs time and money.


Market-ready AI models trained on Mastercard’s anonymized and aggregated global transaction data are another Brighterion innovation that continues to improve results and save customers time and money. These new models offer the advantages of globally accumulated business intelligence to deliver low latency and high accuracy. Currently available for transaction fraud and merchant risk monitoring, Brighterion’s market-ready models are brand-agnostic and ready to operate on most FIs’ systems.

Learn how Brighterion’s market-ready AI is production-ready out of the box to deliver superior financial analytics and immediate return on your investment.

*All Mastercard transaction data has been aggregated and anonymized when used to build Brighterion’s AI and ML models.