How Data Enrichment Improves Fraud Detection & Prevention

How Data Enrichment Improves Fraud Detection & Prevention

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by Florian

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.

Difference Between Manual & 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.

What is Data Enrichment 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 enrichment, as it allows you to complete a picture without necessarily asking users to submit the information themselves.

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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.

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 previous data breaches?
  • Is it used to register to social services (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, the IP address of your user can reveal a lot about who they 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 model: relying on complex probability-based classification that remove transparency for the sake of scores.

Whichever the model, by feeding it through a ML system, your simple data point has now been enriched, transformed, and turned into a powerful weapon in your fight against fraudulent users.

Conclusion

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, data enrichment 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, maximize resources to fight against fraud, and outsmart malicious users whose goal is to damage your business.

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Frequently Asked Questions about Data Enrichment for Fraud Detection

How much does data enrichment cost with fraud prevention software?

Most fraud prevention software include 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.

What industries use data enrichment?

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 datapoint as possible.

You might also be interested in reading about:

Learn more about:

Data Enrichment | Browser Fingerprinting | Device Fingerprinting | Fraud Detection API | Machine Learning Fraud

Sources used for this article

  1. Tech Crunch:  APIs are the next big SaaS wave
  2. University of Cambridge:  Explainable Machine Learning for Fraud Detection
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Author avatar
Florian
Communication Specialist

Florian helps tech startups and global leaders organise their thoughts, find their voices, and connect with customers worldwide.


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