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Four ways financial services organisations can use a graph database

(Image credit: Image source: Shutterstock/wrangler)

Financial services firms are beginning to see the impact of graph databases across a number of functions. From fighting financial crimes, preventing and responding to cyber threats and ensuring compliance, financial services organisations are finding graph databases to be essential in solving data-driven problems. 

Traditionally, banks, credit card companies, insurance firms and other financial services companies have relied on relational databases (RDBMS) combined with other business intelligence tools to process and manage the massive amount of complex data produced on a daily basis. While relational databases are useful for handling basic data collection, storage and analytical processing needs, such as analysing human resources records, this technology fails when it comes to delivering the speed and responsiveness needed for processing complex queries.

Unlike relational technology, which is based on a hierarchical system of tables, rows and columns, graph databases rely on graph theory (opens in new tab) which consists of “nodes” to abstract value and find relationships within the data, allowing users to build sophisticated models in a shorter amount of time. With a graph database, financial services firms can uncover patterns and valuable connections between datasets that relational technology is simply incapable of detecting.  

Financial organisations around the world are realising that, in terms of managing data, they must move past traditional methods. Graph databases are helping financial services organisations gain a competitive advantage from digitisation to drive revenue, minimise costs and build stronger relationships with their customers. Here are four examples of how graph technology can help financial service firms: 

1. Preventing and detecting financial fraud

It’s more difficult than ever for financial services organisations to detect and prevent fraudulent activity and the problem is getting worse. Standard anti-fraud technologies use discrete data, which is useful for catching individual criminals acting alone, but they fall short when it comes to detecting fraud rings. Furthermore, discrete methods can also produce false positives, which can waste time and resources, negatively impact the customer experience and result in lost revenue. Fraudsters have also adapted their techniques to attack the weaknesses of these traditional solutions. For example, criminals will use synthetic accounts and fake names to hide their behaviour and access a financial system through multiple accounts without being detected. 

To keep one step ahead of the bad guys, some of the world’s leading financial institutions are using graph technology to identify fraudulent activity from fraud rings and uncover synthetic or stolen identities. Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy in real time. 

2) Anti-money laundering

Managing the risk of money laundering brings up a similar challenge. Firms need to track where funds are coming from and where they are going. However, instead of moving money directly from one place to another, criminals use indirection and sophisticated schemes to steal assets.

Unfortunately, many traditional systems aren’t designed to keep up with this activity, which means that detecting money laundering schemes can create a great deal of manual effort for teams, spending hours sorting through the data.

Anti-money laundering (AML) teams at financial services organisations are using graph databases to model companies, accounts and transactions as a graph to discover instances of money laundering. By graphing the relationships between all of these entities, teams can track how and where funds are moving through automated Cypher queries that map to traditional money laundering behaviours. Once a suspicious transfer of funds occurs, the system can automatically flag the transaction for review by an AML analyst.

3) Customer identity and access management

IT departments at finance firms implement identity and access management systems to store information about individuals and their authentication, authorisation, roles and privileges. 

Unfortunately the system privilege structure, which is either represented in Active Directory or an LDAP directory, becomes hard to manage because of the complexity of the infrastructure. This can create significant challenges for IT, in terms of managing multiple changing roles, groups, products and authorisations and determining who has access to what permissions. For example, an investment banking platform has very complicated permissions for managing high-profile financial information and transactions both internally and externally. 

With the help of a graph database, information security groups at financial services organisations can seamlessly track all identity and access results in real time, connecting data along intuitive relationships. In addition, queries can take place in real time as users are added or removed for fraud prevention. With an interconnected view of data, firms have better insights and controls than they previously did. On top of this, graph technology makes identity and access management faster and more effective without complex, hours-long queries into the database.

4) Cybersecurity

Protecting valuable customer information, safeguarding business assets from cyber threats, and improving data sharing between public and private groups are all top of mind in the financial services industry, and all fit under the broad umbrella of cybersecurity. 

Unfortunately, these efforts have become increasingly difficult due to the complexity of the data centre. For example, an average bank will have multiple versions of Windows, Linux and other systems as well as multiple desktops. IT must have visibility into which customers and employees are accessing specific applications and what resources are useful to them.

With a graph model, IT can track potential cyber threats across their entire infrastructure, identify points of failure within a network before they become dangerous and warn cybersecurity experts of typical attack patterns, even if they don’t appear to be threatening. By strengthening security across a financial services firm, graph technology allows firms to conduct business as usual while protecting mission-critical systems from cyber criminals.

Graph databases are the future 

Financial firms can no longer settle for out-of-date technology systems, which lock away valuable data in miscellaneous silos. Instead, firms need a seamless way to connect the dots between systems to gain a connected view and the ability to derive value from data relationships.

With graph technology, financial services firms can detect fraud rings more accurately, connect various systems and data sources and oversee identity and access management, while at the same time boost customer engagement and drive sales revenue.

Utpal Bhatt, VP Global Marketing at Neo Technology (opens in new tab)
Image source: Shutterstock/wrangler

Utpal Bhatt is the VP Global Marketing at Neo Technology.