Galileo.XAI, the anti-fraud platform that easily creates and optimizes your rules and scores.

Leverage machine learning, artificial intelligence, and graph visualization to help your team find more fraud, faster and faster.

Billion €
Fraudolent Transactions
Online payments frauds
Increased of fraud


Fraud is a huge problem in various sectors as banking, finance and insurance and many others.

The overall amount of fraudulent transactions was 1.8 billion euros in 2020.  The 80% of fraudulent transactions derive from unregistered cards, that are online payments. The number of fraudulent transactions has increased by 4.3% compared to the overall number of transactions.

Detecting and preventing fraud is a huge challenge given the large variety of fraud types and the volume of transactions that need to be reviewed and manual or rules-based systems can’t keep up.


Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.

Near real time analysis and offline analysis of transactions don’t use connected data using a “poor context” to find potential fraudulent behaviours. Without using connected data we are not able to understand the “context” around a transaction while the connected entities are fundamental to improve our outcomes and get new insights.

With graphs we can explore all the relationships at scale to get a better understanding of the potential fraud as we know that in general fraud is not the result of a single isolated entity but is composed of multiple entities acting together.

Galileo.xai Key benefits


Integrate Graph-AI capabilities both during near real time validations and offline validations.


Use graphs as a post-hoc explainability method for AI systems.


Use pattern matching to design and implement better and more readable rules


Use graph capabilities to implement new and more accurate scoring systems


Galileo XAI is applicable of many different cases of frauds.

Your challenge, our solution!

Not all fraud can be prevented. Even in the most secure organizations, it is likely that some type of employee fraud will eventually occur. Consequently, quick detection offraud is vital to protecting an organization from potential damage.

Procurement fraud is notoriously difficult to detect and investigate, because it takes so many forms and can be driven by any number of actors, internal or external, at any point in the procurement life cycle. Manual detection is futile. Only the right combination of advanced analytic techniques can arm large organisations to battle the fraudsters.

Today’s sophisticated fraudsters escape detection by forming fraud rings using stolen and synthetic identities. To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them.

Credit card fraud is becoming increasingly common with the ubiquity of digital financial applications in our lives, which expands the potential attack vectors. Credit card companies need to take measures to ensure extra safety for their customers by using cutting-edge technology to detect potential fraud. Beyond relieving consumers of financial fraud anxiety, this helps businesses avoid the costly consequences of fraud. 

Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Regrettably, auditing tax declarations is a slow and costly process. Leverage machine learning, artificial intelligence, and graph visualization to help your team find more tax fraud, faster and faster.


Why Neo4j? With 15 years of experience on graph we chosen the best technology in the world.
Understanding the connections between data, and deriving meaning from these links, doesn’t necessarily mean gathering new data. Significant insights can be drawn from one’s existing data, simply by reframing the problem and looking at it in a new way: as a graph.
DT combines the proprietary AI technology Deep Tensor, a deep neural network that is especially suited to datasets with meaningful graph-like properties with Knowledge Graph (Neo4j). Deep Tensor converts graph-structured data to a form of mathematical expression called a tensor and performs deep learning to achieve the highly accurate findings.

Discovering Connections
with Graph Database Technology