Addressing Key Challenges in Financial Services with Neo4j
In today’s regulatory environment, financial services firms are beginning to experience the impact of graph databases across a number of functions ranging from fighting financial crimes, preventing and responding to cyber threats and ensuring compliance. Meanwhile, as the continuous digitization of processes requires financial services firms to evolve their customer engagement strategies to meet rising customer expectations, graph databases are helping financial services firms gain competitive advantage from digitization to drive new sales, reduce costs and build closer relationships with customers. This white paper illustrates how financial services organizations are using graph databases, specifically Neo4j, to effectively solve these problems.
Graphs for Better Risk Management and Regulatory Compliance:
The financial crisis of 2008 showed that financial assets are incredibly complex and become even more so as they’re bundled together and sliced into various sub-assets that are repackaged and resold. For example, a firm might offer an option, which is an instrument on top of an asset. It might also have a mutual fund that owns stocks and hedge funds, and it might have options positions on the same stock, and so on. This results in incredibly complex interdependent systems where risk both compounds and obfuscates simultaneously, creating a regulatory compliance nightmare. It can look like there’s even exposure across ten assets. If you could drill down to the root asset, you might find that you’re 90% exposed to one particular factor, but the exposure is just veiled by the layers above.
As a fix, financial services firms are building asset graphs with Neo4j to get a complete, clear and traceable understanding of relationships among different kinds of financial assets. Such an asset graph provides the firm with a complete understanding of risk. In addition, firms are also using asset graphs to perform derivatives pricing in real time where the price calculation formula takes into account the many interdependencies between assets, and therefore accurately reflects the risk/reward ratio
Information Management with a Metadata Graph:
Regulatory compliance requires financial services firms to have visibility into data, information and process flows. The Sarbanes-Oxley Act (SOX), for example, requires public firms to understand who has access to what data, what data resides in which systems, and how data flows across the organization. This can be a challenge because the same data can be replicated across many different systems. For example, security master data, which might be sourced from Bloomberg, might spread to 150 systems, while the master set of the firm’s products might be replicated across 200 systems. Knowing your data lineage and how data moves is not only necessary for regulatory compliance efforts, but it also helps speed up projects that depend on that data.
Financial services are using Neo4j to model their data lineage and data flows as a graph to get a complete understanding of data and systems across the organization.