The power of data enrichment in fraud and AML

Simon Taylor

Head of Strategy and Content

min read

•

October 9, 2023

The Power of Data Enrichment:

The adage "quality over quantity" has never been truer in a world drowning in data. Enter data enrichment – enhancing raw data with additional information to provide a clearer picture. It's like having a magnifying glass that highlights the most crucial details.

All risk problems are data science problems.

But it's not just about magnifying; it's about highlighting the right details.

You need the best data from the best providers to be available when you need it in the form you need it. This could be phone, email, address, credit card, bank account, and social security numbers (SSN) or local central IDs.

At Sardine, we don’t just aggregate data; we curate it. With over 35 specialized providers in our arsenal – all vetted by our in-house fraud and compliance squad – we’re on a mission to provide data that matters.

Whether it's data from open banking or those elusive signals from a "bank consortia," we’ve got it.

One example: The Gold in Bank Consortia Data

In my first week at Sardine, our co-founder Adi told me, “If you could have told me in my Fintech days we could have Bank Consortia data, I’d have been blown away.”

So, why all the fuss about bank consortia data? Imagine spotting a potential fraudster not just by their current actions but by delving into a history where they might have been implicated before.

This data acts like a memory, recalling instances that help us make better decisions now. Combine this with open banking data, email history, or telco data, and you can stop a bad actor at onboarding before they become a problem or decline the right transaction at checkout.

Proprietary Signals and Third-Party Data.

We take pride in our in-house device and behavior signals. But when blended with our enriched third-party data, it creates a detailed landscape for accurate fraud detection. It's not about having endless data; it's about having the right mix.

Making the data useful.

We need three things to make the data useful for the fraud squad.

Performance: Data and the providers must deliver. The primary KPIs are better check-out conversion, reducing chargebacks, and manual work. Data providers need to deliver, and that’s our key metric when selecting providers.
Curation: Not All Data is Created Equal. In the information age, discernment is key. At Sardine, each data point and provider is collected and vetted. Our experienced team ensures that the data you rely on is relevant and accurate.
Customization: We get it; every business is unique. Hence, the choice is yours whether you want to consume our enriched data via an API or craft custom rules through our intuitive dashboard. We provide the tools; you wield them.

Combining signals is the key

Imagine a fraudster has stolen a phone. They’d pass the phone number check and possibly name and address if those data points are stolen. But what about the email? What about the device they’re using? You could miss the crucial data point without pulling it all together.

The nature of detection is looking for needles in the haystack. This happens to be something AI is uniquely good at. It spots inconsistencies, but like all data science, it requires lots of data and high-quality data.

Data science needs data access

To find needles in a haystack, data scientists need data warehouse access, but that’s surprisingly hard to get. Most fraud and risk platforms provide a risk score but might not share the data. The problem is every business has a unique risk profile. Their data scientists are often best placed to build new models, but its still rare to get access to the right data with the right quality.

Enter Sardine. We’ve curated over 30+ data providers and hand-picked them for performance, quality, and the ability to customize those data sets.

Need to Make the Business Case?

Sometimes, explaining the intricacies of fraud prevention to those holding the purse strings is tough. So, for a touch of fun, we asked our internal AI chatbot, Finely, to make the business case: