Using an FI’s Historical Data Vs Training AI on Mastercard Transaction Data

The AI Express implementation approach gets customers ready for deployment in a few months

Every day, financial institutions (FIs) are exposed to credit and fraud threats. According to PYMNTS.com, nine out of ten acquiring banks reported a rise in transaction fraud during COVID-19. While interest rates are rising, U.S. lenders are conducting business, and household debt has reached an all-time high of $15.84 trillion. These issues can now be swiftly and correctly handled using off-the-shelf artificial intelligence solutions. These models may be put into production in as little as 30 days and are ready for instant worldwide deployment.

The range of transaction data is enormous, thanks to Mastercard's extensive worldwide network of 210 nations and territories. One of Mastercard's key values is the use of transaction data for financial data analytics while upholding client privacy. In order to create the AI and ML models, all Mastercard transaction data has been combined and made anonymous.

Creating conventional and/or unique AI models

Some FIs have trouble obtaining the appropriate financial data for model development and training, or they might not have the historical data required by developers.

To address this need, FIs must extract enormous datasets while assuring accurate labeling and efficient data transport. The number of required datasets could be in the hundreds, necessitating a substantial time commitment from the FI. These are used to train the new model to find anomalies pertinent to the unique challenges facing the business.

After that, it takes the custom model six to eight weeks to complete, including testing, before it is prepared for deployment.

Launching market-ready, self-learning AI models

Advanced AI and ML technologies are used to build models that are ready for the market. These readymade solutions go beyond the business intelligence found in a FI's own historical data since they are trained using Mastercard's business intelligence, which is derived from processing more than 150 billion transactions annually. The model now has additional knowledge thanks to the use of this stronger data collection.

Market-ready AI's main benefit is that it saves financial institutions (FIs) time and resources because the model has already been created, trained, and exhibits excellent accuracy rates. After initializing for 30 days with a small sample of the FI's own transaction data, the model is ready for deployment. The adaptable API interface is then tailored to the client's requirements.

A place for unique AI

The AI Express implementation approach gets customers ready for deployment in a few months and has the process of creating bespoke models down to a science.

In situations where innovation and experimentation are required for a particular or unique business challenge, custom-built AI models don't have to be difficult to use.