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How to use Neo4j to Monitor Customer Fraud in a Fintech App

We had a problem. In a fintech startup that has products for both B2B and B2C markets, there appeared some less than honest users who were trying to game the system via multiple accounts.

It became clear that these users were connected in one of two ways-via either the same IP address or the same payment card number. As the product and marketplace continued to grow, the data amassed in three distinct SQL tables-one for user names, one for IP addresses, and yet another for payment cards.

Eventually, detecting malicious actors and preventing fraud became mired in queries upon queries, requiring more and more time and resources to weed out. We needed a better solution, both for our sanity as developers and for the continued safe growth of the products.

In 1736, Leonard Euler formulated and proposed a solution to what is now a classic problem in graph theory. In essence, the goal was to cross all seven city bridges in Königsberg without crossing any one bridge twice.

This guy was a busy and prolific mathematician, publishing over 500 books and papers throughout his life. At first, he thought the problem was trivial. But it seemed to gnaw at his curious mind. It took over 21 paragraphs in his mathematical to come to the following conclusion:

At its most basic, a graph is an abstract mathematical object representing a set of vertices (points or nodes) and edges (lines) that connect them. For example:

Two of the biggest social networks clearly illustrate the differences between directional and non-directional graph structures. Facebook’s ‘friend’ connection is an ‘edge’ that connects two people only after they both acknowledge their connection.

In the Twitter social graph, however, relationships can be in one direction—Bob follows Alice—or bi-directional—Bob follows Alice, and Alice follows Bob.

We’ll talk a bit more about directions later.

In a database built on graphs, there is no such thing as a table with strictly defined fields. Rather, it operates with a flexible structure made up of vertices and edges with their own models with fields.

Cypher is the programming language used for writing queries not only for graph database Neo4j, but also for MySQL. So if, like us, you run up against the limits of efficiency as your MySQL database grows in size and/or complexity, you’ll quickly be able to understand query construction in Neo4j.

Let’s walk through a simple example before we take a crack at that problem of querying users.

Fetch all the vertices:

Creating a vertex with a Movie label and title property:

Creating a connection between two vertices:

Adding new properties to the top:

Deleting all vertices and edges:

To detect and prevent fraudulent activity on our platform, we need to quickly connect different users who are using the same IP address or payment card. And at any given time be able to see all these links for any selected user.

For this example, we will show how to connect users with the IP address as the criterion in our Neo4j graph database. So each node will be a user and each edge will be formed between them based on them having the same IP address.

For the user, we will use the following fields:

name: string id ips: array

All the nodes will be labeled Person. For edges, we only store an array of IPs, because in some cases the users have more than one identical address. These edges are marked with the IP label.

So, let’s make a small three-person graph to start:

Now we can MATCH (n) RETURN n to see our graph:

All good so far, now we’re going to add a new user. So we have to create their node and find out if they are using the same IP address as anyone already in the database. If they are, then we want to create a labeled edge between them.

Note that we want to exclude the new user so that we don’t make a loop edge back to themselves. And so our graph continues to grow:

Now, we need to start throwing some new IP addresses into the graph. So let’s add another user with a different address:

And now we have Vasya sitting all alone:

Now we can see what happens if Andy adds another IP address to his array that is the same as our lonely user Vasya.

The point of this is to add a new IP address to Andy’s array and simultaneously search for other users with that new address AND create a new edge between them based on that new IP. And so we get:

Andy turns out to be a bit of a nomad, so each time he logs in from a new IP address, we can update it AND search for new connections based on that new address:

At this point, no one else is using the same IP address, so no new edges are formed. But let’s say that Vasya decides to follow Andy:

This creates an unnecessary edge. Remember, each edge is an array of IP addresses so there is no need for this.

MATCH ()-[r]-() WHERE ID(r)=35 DELETE r

Without getting too deep into all the permutations that can grow out of this graph structure, the goal is to create a clear system of connections as the user base grows (more nodes) and the IP addresses also increase (edges).

To see this code in action, let’s add one final user:

And now, the moment of truth:

Back to finding solutions to our problem of fraudulent users. In a database of thousands, you can easily write queries to see if there are any connections between users.

If we suspect two users of acting together on the platform, we can:

And we can look at the results in a table:

As we’ve shown, Neo4j is a sensible and efficient database option for categorizing, connecting and querying a customer base of any size. By using the same Cypher language as MySQL, it’s a short learning curve for experienced developers to make the transition to Neo4j.

Fraud prevention and protection are a core priority of all digital banking products. There is no more rapid way to dissolve your customer’s trust in a product than exposing their bank accounts to malicious actors.

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