The Problem: How to clean and enrich messy transaction data in real-time

The data infrastructure underlying the card industry hasn’t changed in decades—and, believe it or not, this isn’t doing banks (aka, card issuers) or consumers any good. This is especially true today when fraud is at an all-time high. 

The team at Spade knew that this could no longer be the status quo. So, they set out on a mission to bring long overdue disruption to the card industry by giving card issuers better, more granular, and more insight-driven data to work with. After all, relying on incomplete data to monitor and manage transaction health simply couldn’t cut it anymore. 

From Spade’s perspective, the key to bringing greater legitimacy, accuracy, and transparency to card transactions—while simultaneously catching instances of fraud early on—is to provide more context and data on transactions, including the exact location where those transactions take place. This additional layer of data can be used to optimize fraud detections models, refine spend control measures, and even streamline consumer rewards programs. In other words, a real game changer for the industry.

Unfortunately, most banks don’t receive granular location information, like a merchant’s address, at the moment of transaction. Without this kind of context, it’s virtually impossible to know exactly where consumers spend their money. Plus, it makes it infinitely more difficult to personalize the consumer experience, incentivize rewards redemption, and nip fraud in the bud. 

While there are clearly a number of benefits that come with attaching a verifiable location to card transactions, the truth is, detecting and stopping fraud is what really keeps card issuers up at night. However, given just how outdated the global card data infrastructure is, establishing a location-based layer for all transaction data as a new industry standard has been no easy task. That is, until now thanks to the work of Spade’s team.

The Problem-Solver: Spade

Spade is a fast-growing startup that’s on a mission to power the future of finance with superior transaction data. But what does that mean? In short, Spade’s transaction enrichment API, backed by real data, helps bring instant clarity and context to card transactions. The company is quickly revolutionizing what happens at the “transaction moment” by accurately matching merchant, category, and geolocation details—acquired directly from verifiable sources—to all card transactions that pass through their API.  

To turn this into a reality, the team at Spade had a big choice to make. Were they going to continue sourcing, scraping, cleaning, and cataloging POI data on their own? Or were they going to look for a data partner to help them fill this gap? 

“We had our own data but it was just not as broad as we needed,” explained Oban MacTavish, CEO at Spade. “Scraping POI data is a complicated, expensive, and time-consuming process, and we needed a data partner who could address our POI data needs at scale.”

The Solution: SafeGraph Places

“After vetting a number of data providers, we ultimately chose to work with SafeGraph, first and foremost, because of the sheer breadth and depth of their POI data,” continued MacTavish. “We wanted to work with an expert in location-based data who would serve as a trusted data source for us and hold a high bar for what a location actually is.” 

Before working with SafeGraph, Spade was scraping anywhere between 500k to 1M locations on their own; after joining forces with SafeGraph and leveraging the Places dataset, they now had access to millions of merchants across the U.S. This enabled them to scale their offering instantly. 

Although data coverage helped make this an easy decision for Spade, it was SafeGraph’s dedication to perennial data quality that really stood out. While surveying other vendors, the Spade team realized that not all vendors maintained the same quality standards. Some even dilute their own POI data with inaccurate merchants. This is a big problem because widespread mistakes, like closed or inaccurate locations in a dataset, can quickly become a costly error.

But that’s just the technical part of working with SafeGraph. MacTavish continued, “We appreciated SafeGraph’s flexibility to build an agreement that made sense for us and their responsiveness whenever we needed any kind of support.” From delivering the data as flat files to reacting quickly to feedback, Spade has continued to be impressed with SafeGraph’s ability to come up with win-win solutions for the partnership. “We spend a lot of time with the data—and whenever we spot issues, SafeGraph ensures they get fixed quickly.” 

Finally, as the cherry on top, one other detail set SafeGraph apart from the competition. “We’re a highly specialized startup with a very specific use case that not every vendor can support—or is even necessarily willing to make the investment in supporting,” explained MacTavish. “Even though this was a new use case for SafeGraph, too, it was clear from the start that they wanted to innovate with us and help our business succeed.”

The Result: Enriching card transactions with location-based data

“A big part of what we do, as a merchant data company, is connect transactions to a variety of data sources, with POI data used specifically to verify that businesses are real,” summarized MacTavish. “Although matching locations to transactions is just one piece of the Spade pie, it’s a key piece of our product—our services run on POI data.”

While this industry-disrupting startup is still very much in its early stages, Spade has successfully set the foundation to revolutionize an ancient card data infrastructure that relies too heavily on inaccurate and incomplete data. By showing the industry what’s possible when more dimensions are added to transaction data, they will enable credit card issuers to generate more swipes, reduce slippage, offer more (and better) rewards, and of course, stop fraud. 

MacTavish concludes, “Our business needs to exist; the credit card industry is overdue for a serious overhaul. However, without SafeGraph data, we couldn’t do what we do today at the quality and scale our customers expect from us.”