How Data Enrichment Improves Fraud Detection & Prevention
by Florian Tanant
At SEON, we pride ourselves on our data enrichment processes. But the term can be confusing to business owners – and even to some fraud managers. Let’s dive into what it means to enrich data and how it can protect your company in the long run.
Manual vs Automated Data Enrichment
Data enrichment is about gathering extra information based on a few data points. A Google search using someone’s name, for instance, constitutes manual data enrichment. An automated solution will aggregate extra data automatically from various sources, such as open-source databases or social media networks.
Why Gathering Data Is Important in Fraud Detection
In fraud detection, the more data you have about your users, the more informed your decisions can be when calculating risk. The process of gathering information often comes from data augmentation, as it allows you to complete a picture without necessarily asking users to submit the information themselves.
What Kind of Data Enrichment Tools Work to Fight Fraud?
Getting more data is always good. But not all tools will help you get the right information. Here is a breakdown of the most commonly used data enrichment tools in fraud prevention.
But before you go into a deep dive, maybe you would like to see an example. Type your email address or phone number in the field below to see how SEON enriches data to spot potential fraudsters.
Email Address Data Enrichment
Chances are that all your users will need to sign up with an email address. This simple data field can reveal a lot by comparing it with external databases:
- Is the address free or paid?
- Is it disposable?
- Is the domain registered?
- Has it been involved in any data breaches?
- Was it used to register to social media and platforms (Facebook, Instagram, Spotify etc…)?
Deep social media profiling and domain verification, for instance, is a science SEON is proud to offer through our email risk analysis API.
IP Address Data Enrichment
Similar to email addresses, an IP lookup tool can reveal a lot about who your users truly are.
- Where is the user based?
- Are they connecting through open ports – communicating with other servers?
- Are they using proxies, VPNs or TOR?
- Is the IP on any spam blacklists?
- Datacenter IP or residential connection (belonging to a homeowner)?
BIN Number Data Enrichment
Another great example of enriching simple data points through external data sources. BIN (Bank Identification Numbers), also known as IIN (Issuer Identification Numbers) can tell us a lot about a card:
- What bank issued the card?
- What kind of card is it?
- What is the bank’s phone number?
- What is the card’ level (ATM only, Gold, Platinium, World Elite or Infinite depending on the provider)?
Device Data Enrichment
This is what, at SEON, we call Device Fingerprinting. Quite simply, it’s the ability to tell a lot based on the device the user connects to your site with:
- Has the user connected with the same device before?
- Is it running a virtual machine?
- What kind of browser is installed?
- Is it a mobile or desktop?
The Differences Between Blackbox and Whitebox Data Enrichment
Of course, all the points above are easy to fake or enter with stolen information. Which is why data enrichment is only truly valuable in fraud prevention when combined with a solution designed to understand it.
How is that possible? In two words: machine learning. Now that we have created a large scale data set, it’s time to feed into a ML model to analyze, report, and reveal insights. At this stage, organizations may create different models based on their needs.
- Whitebox solution: A machine learning model that delivers readable rules through a Decision Tree algorithm. Each applied rule creates a new branch where the nodes are clear parameters.
- Black box machine learning model: relying on complex probability-based classification that remove transparency for the sake of scores.
Whichever the model, by feeding it through an ML system, your simple data point has now been enriched, transformed, and turned into a powerful weapon in your fight against fraudulent users.
Data enrichment isn’t just an option for fraud prevention: it is one of the most crucial processes which allows you to get a clear picture of who your users are – whether it’s in the travel industry or for an online gambling platform.
However, gathering data is just one part of the process. It is meaningless without proper analysis. And the only way to pore over this data to turn it into insights is through machine learning tools. Combining data enrichment with machine learning is what improves risk-based decision making, maximizes resources to fight against fraud, and outsmarts malicious users whose goal is to damage your business.
Most fraud prevention software includes data enrichment functions as part of their pricing. However, you may find certain providers break the tools into different modules where you only pay for IP analysis, email address analysis or phone analysis. You usually pay on a per API call basis.
Data enrichment is useful in fraud prevention, cybersecurity, but also marketing and online retail. It helps to learn more about your users based on as few datapoints as possible.
You might also be interested in reading about:
Learn more about:
Browser Fingerprinting | Device Fingerprinting | Fraud Detection API | Fraud Detection with Machine Learning & AI
Sources used for this article
- Techcrunch: APIs are the next big SaaS wave
- University of Cambridge: Explainable Machine Learning for Fraud Detection
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Communication Specialist | Florian helps tech startups and global leaders organise their thoughts, find their voices, and connect with customers worldwide.
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