In its latest fundraising round, Spade, the fastest and most accurate provider of real-time merchant intelligence for the card ecosystem, has closed a $10M Series A round led by Flourish Ventures, with participation from a16z, Gradient Ventures, Y Combinator, Dash Fund, and Everywhere Ventures (The Fund).
The infrastructure for processing credit card transactions hasn't changed since the 1960s, yet nearly 2 billion transactions are processed daily on it. During the swipe or tap of a card, the payment data is sent through a network of credit card companies, but due to the data standards that govern how this information is communicated, much of the relevant information about a transaction gets lost in translation.. The card issuers and consumers are forced to make complicated decisions regarding payment authorization, fraud detection, and categorization of card transactions based on a set of unreadable characters that obscure the precise merchant involved in a particular transaction.
The card issuers and consumers are forced to make complicated decisions regarding payment authorization, fraud detection, and categorization of card transactions based on a set of unreadable characters that obscure the precise merchant involved in a particular transaction.
A total of nearly 2 billion credit card transactions are processed every day on an infrastructure that has not changed since the 1960s. Whenever a credit card is swiped or tapped, the payment data is sent through the credit card networks, but due to the data standards that regulate how this information is communicated, much of the relevant information about a particular purchase is lost in the process.. As a result, consumers and card issuers are forced to make critical decisions regarding payment authorizations, fraud detection, and categorizations based solely on an unintelligible set of characters that obscure the precise merchant involved with a transaction
As a result, consumers and card issuers are forced to make critical decisions regarding payment authorizations, fraud detection, and categorizations based solely on an unintelligible set of characters that obscure the precise merchant involved with a transaction
In order for card issuers to improve fraud models, Spade provides card issuers with the fastest and most accurate real-time merchant intelligence available.
An example of this would be when a consumer on a business road trip makes a purchase at a gas station in Lansing, Michigan, which appears to the issuer as "10202LAN12102903".The card issuer must be able to make a vital authorization decision in less than two seconds based on the data contained within this string of text. As a result of a plethora of consumer-focused data that are available, issuers have a clear idea of who a customer is as all sorts of tools, such as machine learning models and deterministic rules engines, are powered by these data. Unfortunately, because merchant profiles are built using the low-quality merchant data available, issuers have limited access to accurate, detailed, and consistent merchant information because merchant profiles are built with low-quality merchant data. This has resulted in false declines, resulting in billions of dollars in interchange revenue lost, and the need to fix false positives and negative customer experiences.
Unfortunately, because merchant profiles are built using the low-quality merchant data available, issuers have limited access to accurate, detailed, and consistent merchant information because merchant profiles are built with low-quality merchant data. This has resulted in false declines, resulting in billions of dollars in interchange revenue lost, and the need to fix false positives and negative customer experiences.

Source prnewswire