Advanced Fraud Detection in Financial Services with ArangoDB and AQL

Estimated reading time: 3 minutes

Advanced Fraud Detection: ArangoDB’s AQL vs. Traditional RDBMS

In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).

(more…)
More info...

Three Ways to Scale your Graph

Estimated reading time: 10 minutes

As businesses grow and their data needs increase, they often face the challenge of scaling their database systems to keep up with the increasing demand.

What happens when your single server machine is no longer sufficient to store your graph that has grown too large? Or when your instance can no longer cope with the increasing amount of user requests coming in?

Read more
More info...

Graph and Entity Resolution Against Cyber Fraud

Estimated reading time: 4 minutes

With the growing prevalence of the internet in our daily lives, the risks of malware, ransomware, and other cyber fraud are rising. The digital nature of these attacks makes it very easy for fraudsters to scale by creating thousands of accounts, so even if one is identified, they can continue their attacks.
In this blog post, we will discuss how graph and entity resolution (ER) can help us battle these risks across different industries such as healthcare, finance, and e-commerce (for example, the US healthcare system alone can save $300 billion a year with entity resolution). You will also receive hands-on experience with entity resolution on ArangoDB.

Read more
More info...

Combat Fraud with Graph

Estimated reading time: 5 minutes

Fraud is one of the most significant issues facing businesses today. While companies have always faced fraud, detecting fraudulent activity has become even more challenging due to increased online transactions. Globally, fraud results in more than $3.7 trillion in annual losses (Murphy, 2022). Fraud comes in numerous forms, including but not limited to money laundering, identity theft, account takeover, and payment fraud. Due to the variety of ways companies can face fraud, they must have a system to protect themselves and their customers.

Read more
More info...

Why Should You Care About SOC 2?

And by the way, ArangoDB is SOC 2 compliant!

Estimated reading time: 3 minutes

first image

While driving along California’s Highway 101 and its billboards, compliance and SOC 2 seem to be an omnipresent – yet challenging – topic. But is it really? And if so, why? In this blog post, we want to share why and how ArangoDB has become SOC 2 compliant.

Read more
More info...
Blog Post Template

Community Notebook Challenge

Calling all Community Members! 🥑

Today we are excited to announce our Community Notebook Challenge.

What is our Notebook Challenge you ask? Well, this blog post is going to catch you up to speed and get you excited to participate and have the chance to win the grand prize: a pair of custom Apple Airpod Pros.

(more…)
More info...
Detecting Complex Fraud Patterns with ArangoDB

Detecting Complex Fraud Patterns with ArangoDB

Introduction

This article presents a case study of using AQL queries for detecting complex money laundering and financial crime patterns. While there have been multiple publications about the advantages of graph databases for fraud detection use cases, few of them provide concrete examples of implementing detection of complex fraud patterns that would work in real-world scenarios. 

This case study is based on a third-party transaction data generator, which is designed to simulate realistic transaction graphs of any size. The generator disguises complex financial fraud patterns of two kinds: 

  • Circular money flows: a big amount of money is going through different nodes and comes back to the source node.
  • Indirect money transfers: a big amount of money is sent from source node to a target node over a multi-layered network of intermediate accounts.
(more…)
More info...

Get the latest tutorials,
blog posts and news: