Finance
HOW THE FINANCIAL SERVICES SECTOR CAN GET VALUE FROM CONNECTIONS
Published by Gbaf News
Posted on August 28, 2017
11 min readLast updated: January 21, 2026
Global Banking and Finance Review
Company
Published by Gbaf News
Posted on August 28, 2017
11 min readLast updated: January 21, 2026
The author is CEO of Neo4j, the #1 platform for connected data (http://neo4j.com/)
Neo4j’s Emil Eifrem believes that financial institutions can gain valuable business advantage by joining the dots in data sets
Emil Eifrem
Disruptive competition by way of Fintech companies and digital transformation are changing the face of financial institutions – to thrive in this environment they must be able to manage and gain understanding from increasing amounts of big data to enable them to continually innovate.
The financial sector is taking large steps forward in integrating systems and disparate data sources to mine data. The real challenge, however, is in creating real value from these connections to gain a broader and deeper visibility into addressing key challenges in the business.
Graph is enabling financial firms to address challenges including fighting financial crimes, such as fraud, preventing and responding to cyber threats and ensuring compliance. Meanwhile, the continuous digitisation of processes is demanding that financial services firms evolve their customer engagement strategies to meet rising customer expectations. Graph databases are helping financial services firms gain competitive advantage from digitisation, while driving new sales, reducing costs and building closer relationships with customers.
The beauty of the graph database here is that financial institutions don’t have to rely on semantically-limited data models and expensive, unpredictable ways of running queries through joins in the traditional relational ecosystem. Graph databases support many named, directed relationships between entities (also known as nodes) that give a richer semantic context. This means that features modelled can be specified in much greater detail, in order to take a deep and meaningful dive into the data. The other big bonus is that graph is also fast.
Financial institutions have much tolearn from the internet giants who have been tapping into the power of graph technology for some time. Google, for example, owes much of its success to its ability to speedily exploit connections in Web documents, enhancing the relevance and expedition that searches can take place.
Graph isn’t only about customer engagement and driving new product concepts. Financial institutions are also using the power of graph technology internally in their data centres, networks and IT infrastructures, creating graph maps of their topologies. Use cases include utilising graphs to help with dependency management, impact analysis, network management, downtime reduction, root-cause analysis and routing, and quality-of-service mapping. Many bank DevOp teams maintain that due to the increasing complexity and interrelated nature of their IT infrastructure, graph databases are the only tool that can truly map and query all of the data they need to model today.
Using graphs to leap over process hurdles
Financial services firms are learning to join the dots that were previously notoriously difficult or impossible.Financial assets, for instance, are highly complex and share intricate, interdependent systems. Graph is proving an important tool in getting a more complete and traceable understanding of the relationships between these important assets and shaping offers accordingly.
Innovative financial firms are also turning to asset graphs to perform derivatives pricing in real time, where the pricing equation needs to incorporate the many interdependencies between items and accurately echo the ratios of risk and reward.
Regulatory compliance is another area where financial institutions need to have a panoramic view of data and flow processes. This can be an enormous challenge as data can be duplicated across a number of different systems. Graphs can help here, as they can easily model data lineage and data flows to deliver a complete understanding of siloed data and systems across the organisation. This is useful for regulatory compliance efforts and also helps speed up projects that depend on that data.
At the same time digitization is changing the financial landscape which is having a major impact on fraud risks. Standard anti-fraud technologies, such as highlighting deviations from customers’ normal purchasing patterns, depend on discrete data. This is useful for catching individual fraudsters, but it flounders when trying to pinpoint fraud rings.
In addition, cyber criminals are continually looking for new ways to launch attacks and stay one step ahead of the establishment. Many steal a number of identities and then use them to create synthetic hybrid identities to open bank accounts, apply for credit cards and credit, for example.
These synthetic identities are hard to track down. Traditional fraud detection solutions will not recognise and red flag them. Real-time graph traversals, however,linked to the right kinds of events can aid financial institutions in spotting possible fraud rings during, after and even before fraudulent activity has taken place.
Money laundering is another major headache for financial institutions, who need to keep track of where funds are coming from and where they are going. Criminals, however, are clever at using indirection to make it difficult to follow the steps from one point to another. Instead of moving the funds from one point directly to another, they make a large number of moves in between, hoping that the financial institution will not notice. Traditional technologies have not been designed to track such zig-zags in movement, which means that it has to be done manually, which is hugely time consuming.
Anti-money laundering teams within financial services, however, are fast recognising the power of graph in being able to track these transactions to quickly build an easily comprehensible picture. Corporate information security groups at financial services firms are also utilising entitlement graphs to audit the entitlement structure and issue queries in real-time to understand often labyrinthine entitlements.
A seamless journey for the digital user
With the wide choice of financial offerings available, customers have become increasingly demanding and their loyalty has to be earned.
The emergence of new technologies has created an ‘omnichannel’ route for customers via PCs, tablets and smartphone apps as well as offline. These channels have been designed to provide a seamless and consistent interaction between banks and their customers across multiple channels. But for this model to work effectively, financial institutions need an accurate picture of their user base. Harnessing the power of connected data (i.e., data relationships) is paramount to driving these real-time, banking and insurance product and service recommendations and personalisation engines that have become key to meeting customer service expectations.
Looking ahead
Banking is up against an enormous challenge in trying to effectively harvest the mass of information that as an industry it churns every day. But with graph database technology it can remove data chaos that can impede financial services doing business and turn it into a very valuable positive.
The author is CEO of Neo4j, the #1 platform for connected data (http://neo4j.com/)
Neo4j’s Emil Eifrem believes that financial institutions can gain valuable business advantage by joining the dots in data sets
Emil Eifrem
Disruptive competition by way of Fintech companies and digital transformation are changing the face of financial institutions – to thrive in this environment they must be able to manage and gain understanding from increasing amounts of big data to enable them to continually innovate.
The financial sector is taking large steps forward in integrating systems and disparate data sources to mine data. The real challenge, however, is in creating real value from these connections to gain a broader and deeper visibility into addressing key challenges in the business.
Graph is enabling financial firms to address challenges including fighting financial crimes, such as fraud, preventing and responding to cyber threats and ensuring compliance. Meanwhile, the continuous digitisation of processes is demanding that financial services firms evolve their customer engagement strategies to meet rising customer expectations. Graph databases are helping financial services firms gain competitive advantage from digitisation, while driving new sales, reducing costs and building closer relationships with customers.
The beauty of the graph database here is that financial institutions don’t have to rely on semantically-limited data models and expensive, unpredictable ways of running queries through joins in the traditional relational ecosystem. Graph databases support many named, directed relationships between entities (also known as nodes) that give a richer semantic context. This means that features modelled can be specified in much greater detail, in order to take a deep and meaningful dive into the data. The other big bonus is that graph is also fast.
Financial institutions have much tolearn from the internet giants who have been tapping into the power of graph technology for some time. Google, for example, owes much of its success to its ability to speedily exploit connections in Web documents, enhancing the relevance and expedition that searches can take place.
Graph isn’t only about customer engagement and driving new product concepts. Financial institutions are also using the power of graph technology internally in their data centres, networks and IT infrastructures, creating graph maps of their topologies. Use cases include utilising graphs to help with dependency management, impact analysis, network management, downtime reduction, root-cause analysis and routing, and quality-of-service mapping. Many bank DevOp teams maintain that due to the increasing complexity and interrelated nature of their IT infrastructure, graph databases are the only tool that can truly map and query all of the data they need to model today.
Using graphs to leap over process hurdles
Financial services firms are learning to join the dots that were previously notoriously difficult or impossible.Financial assets, for instance, are highly complex and share intricate, interdependent systems. Graph is proving an important tool in getting a more complete and traceable understanding of the relationships between these important assets and shaping offers accordingly.
Innovative financial firms are also turning to asset graphs to perform derivatives pricing in real time, where the pricing equation needs to incorporate the many interdependencies between items and accurately echo the ratios of risk and reward.
Regulatory compliance is another area where financial institutions need to have a panoramic view of data and flow processes. This can be an enormous challenge as data can be duplicated across a number of different systems. Graphs can help here, as they can easily model data lineage and data flows to deliver a complete understanding of siloed data and systems across the organisation. This is useful for regulatory compliance efforts and also helps speed up projects that depend on that data.
At the same time digitization is changing the financial landscape which is having a major impact on fraud risks. Standard anti-fraud technologies, such as highlighting deviations from customers’ normal purchasing patterns, depend on discrete data. This is useful for catching individual fraudsters, but it flounders when trying to pinpoint fraud rings.
In addition, cyber criminals are continually looking for new ways to launch attacks and stay one step ahead of the establishment. Many steal a number of identities and then use them to create synthetic hybrid identities to open bank accounts, apply for credit cards and credit, for example.
These synthetic identities are hard to track down. Traditional fraud detection solutions will not recognise and red flag them. Real-time graph traversals, however,linked to the right kinds of events can aid financial institutions in spotting possible fraud rings during, after and even before fraudulent activity has taken place.
Money laundering is another major headache for financial institutions, who need to keep track of where funds are coming from and where they are going. Criminals, however, are clever at using indirection to make it difficult to follow the steps from one point to another. Instead of moving the funds from one point directly to another, they make a large number of moves in between, hoping that the financial institution will not notice. Traditional technologies have not been designed to track such zig-zags in movement, which means that it has to be done manually, which is hugely time consuming.
Anti-money laundering teams within financial services, however, are fast recognising the power of graph in being able to track these transactions to quickly build an easily comprehensible picture. Corporate information security groups at financial services firms are also utilising entitlement graphs to audit the entitlement structure and issue queries in real-time to understand often labyrinthine entitlements.
A seamless journey for the digital user
With the wide choice of financial offerings available, customers have become increasingly demanding and their loyalty has to be earned.
The emergence of new technologies has created an ‘omnichannel’ route for customers via PCs, tablets and smartphone apps as well as offline. These channels have been designed to provide a seamless and consistent interaction between banks and their customers across multiple channels. But for this model to work effectively, financial institutions need an accurate picture of their user base. Harnessing the power of connected data (i.e., data relationships) is paramount to driving these real-time, banking and insurance product and service recommendations and personalisation engines that have become key to meeting customer service expectations.
Looking ahead
Banking is up against an enormous challenge in trying to effectively harvest the mass of information that as an industry it churns every day. But with graph database technology it can remove data chaos that can impede financial services doing business and turn it into a very valuable positive.
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