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Neo4j in finance

Tina Knezevic

Following the previous blog Neo4j in sales, in the last blog related to the series Graph databases and Neo4j we will focus on the world of finance and how graph databases help in solving problems and facilitating everyday financial processes. But first, let’s remind ourselves once again what lies behind the term graph database.

Graph databases

As the name suggests, graph databases are types of databases in which data is stored and displayed in the form of a graph. One graph consists of interconnected nodes.

Neo4j stands out as one of the most popular graph databases. Neo4j is widely used in various industries and solutions, and in this blog, we will highlight the most interesting ones from the world of finance. You can find more details about graph databases and Neo4j in the first blog of this series.

 Figure 1. Display of nodes and connections in a graph database


The world of finance deals with extremely delicate data, including money. The data is distributed through different systems and its security comes first. Therefore, it is extremely important for a successful business that data can be obtained in a simple way without compromising its security, privacy and dynamicity of the entire business. That is why graph databases have been chosen for many financial solutions. They not only help in the fight against financial fraud but also enable companies to gain an advantage over the competition by analysing their own revenues and sales.

Risk management

What contributed to the global financial crisis in 2008 was the lack of timely risk data during the collapse of the company Lehman Brothers. Without standards for properly aggregating risks in their financial positions, banks have not been able to quickly assess the dependence of their different holdings on Lehman shares and assets. To prevent such things, financial institutions must be provided with risk data, which includes the visibility of data links, all the way back to the authoritative data sources. Such visibility is possible only with financial data standards and modern technology that would allow the display of such complex connections and paths.

One of the solutions for risk management is to create graphs of financial assets in order to obtain a complete, clean and traceable understanding of the relationships between different types of financial assets. Such graphs allow financial institutions to fully understand risk. In addition, firms use asset graphs to determine real-time derivative prices by taking into account many price interdependence formulas and thus accurately maintain the risk / reward ratio.

Figure 2. Risk management using graph databases

Financial fraud

It is becoming increasingly difficult for financial services to identify and prevent financial fraud. Standard anti-fraud technologies use discrete data, e.g. in tracking deviations from common consumption patterns. This is sometimes enough to catch individual scams, but such systems are ineffective in recognizing the so-called fraud rings. In addition, a number of methods based on discrete data are subject to false positive results, which greatly affects customer satisfaction.

Financial fraud is becoming increasingly difficult to spot as criminals introduce new ways to deceive systems on a daily basis. For example, they create synthetic accounts to carry out unrelated, and in fact highly correlated, activities. Personal data is often stolen from several different people, and then their data such as address, email, phone number, etc. is mixed up and new synthetic users are created, which are then used to open user accounts or credit cards. Traditional ways of defending against fraud cannot spot such ways of fraud. Therefore, financial institutions must monitor customer-related data and their accounts in order to spot such unrelated links.

More and more financial companies are opting for graph databases to track data about users, devices, locations and other activities, thus identifying and preventing fraud that occurs by creating synthetic data. This method is more efficient than modelling relational databases that require frequent, complex, and time-consuming JOIN operations to get results. As the efficiency and speed of query execution is extremely important, so as not to affect customer satisfaction, graph databases are a logical choice to solve this problem because they help detect fraud rings during or even before a fraud transaction has taken place.

Figure 3. Spotting the chain of financial fraud

Money laundering

A challenge similar to the previous one is to prevent money laundering. Financial institutions need to know where the funds come from and where they are directed to, but even for that criminals use indirectness to make it as difficult as possible to track money from one point to another. Instead of moving money from point A to point B, they use a dozen other points in between so no one would notice the transaction.

Traditional solutions are not designed to follow many intermediate steps between transactions, which means that detecting money laundering requires a huge amount of manual effort. However, with graph databases, such a problem can be solved, since it is possible to very easily monitor all transaction paths and thus determine potential money laundering. All this is achieved through Cypher queries that map all paths between a starting node and ending node and thus detect fraud schemes.

Figure 4. Money laundering scheme

360 ° customer overview

Consumer expectations are growing every day. Customers expect personalized services that are tailored to them and their needs. This is a problem not only for sales, but also for financial systems. Financial systems want to meet customer needs and they are looking for a way to use and integrate all available online and offline tools to increase revenue. In order to achieve this, they need data that is distributed through various systems within the company. Graph databases are the perfect way for financial systems to use them for Master Data Management. With their help, financial systems can get a detailed insight into all interconnected data and monitor user activities and habits in order to better plan and predict their consumption, and thus improve the revenues of the companies themselves.

Figure 5. 360 ° customer overview


The modern age poses a major challenge for financial systems that handle highly sensitive data. It is therefore extremely important to take advantage of modern technologies that help deal with burning issues such as fraud or personalization. Graph databases are one of the ways to gain insight into all available data and reveal information that is invisible to the eye, and crucial for the business of modern financial systems.

If you want to know how graph databases deal with similar and other problems in the world of telecommunications and sales, read our blogs Neo4j in sales and Neo4j in Telecoms and find out how graph databases can improve your business as well.


[1] Nav Mathur, Graph Technology for Financial Services: How Top Financial Firms Harness Connected Data to Increase Their Bottom Line, [29.06.2022]