The Power of Knowledge Graphs in Transforming Financial Services
Want to be famous? It may take a lot more than talent. In a recent examination of the pioneers of abstract art, researchers from Columbia University and HEC Paris discovered that creativity didn’t directly correlate to fame. Instead, artists with large diverse networks of personal and professional contacts in different industries were statistically more likely to become famous.1
The same structure can also be used to uncover insights from complex data sets in the financial sector. Traditionally, data has been stored in relational databases, organized as a set of tables with columns and rows with pre-defined relationships between them.4 However, this method has become increasingly limiting as relationships and entities grow more complex.5
Graph databases promote simpler and more intuitive representations of connected data in ways the traditional method could not.6 Further, natural language processing (NLP) enables unstructured data and information points to be represented in a comprehensive view of nodes and relationships between the data.
But when employing a knowledge graph, the more data accumulated, the more insightful it becomes.7 As data is ingested, this process allows knowledge graphs to identify individual objects, understand the relationships between them, and compare this to other integrated data sets. Once a knowledge graph is complete, users can retrieve information from queries.
Knowledge graphs are widely used today, by companies such as Facebook, Apple and Google to power their search engines,8 yet the tool is still gaining traction in the financial sector. Notably, the power of Google’s search engine is in their knowledge graphs, comprised of more than 500 million objects and combining disparate data sets, the technology gives users arguably, the most comprehensive information and insights.9
Given this potential, knowledge graphs hold significant value outside of informing search engine results. In the financial sector specifically, enterprise knowledge graphs provide the potential to operate more efficiently, connecting complex data sources, and when built and managed correctly, unlock new analytical capabilities to the extent that they can be regarded as the “brain” of an organization.10
As knowledge graph technology becomes more powerful, the technology has the potential to unlock opportunity across the financial ecosystem and optimize processes to help institutions foster development across all areas in the future. The following are possible use cases.
Improve tax strategy. The taxation of multi-national companies is a complex field, influenced by the legislation of many governing bodies. For multinational firms, knowledge graphs may streamline income tax calculations by condensing over tens of thousands of pages of global tax law text into a set of simple logical relationships, so that corporations can perform better tax planning.12
Create in-depth operational analyses. Large financial institutions can have tens of thousands of customers, all of which have relationships with not only the institution but also across the institutions’ customer base as well as outside it. Knowledge graph technology has the potential to leverage insights from these relationships and networks to identify potential suppliers or clients that would be affected should another client endure financial distress.13
Improve the customer experience. Knowledge graphs have the potential to improve customer relationships across multiple touchpoints, aggregating data from across lines of business to help create a 360-degree view of any one customer’s unique profile and experience. This concept, known as Customer 360, guarantees consistency in customer information across all departments, which can help break down silos and identify unique patterns and preferences to ensure continuity throughout the customer journey. In addition to strengthening current relationships, knowledge graphs’ predictive power can also be applied to identify desirable potential clients, promoting continual growth for the firm’s client base.14
Enhance Anti-Money Laundering (AML) and Know Your Customer (KYC) processes. AML regulations are mandated by both national and international authorities that place a wide variety of screening and monitoring obligations on financial institutions. These obligations include KYC, a set of standards used to verify a customer’s identity and the level of money laundering risk they may represent.15 Knowledge graph technology can help strengthen such efforts, providing a comprehensive view into a client, and the context of their network. For example, BNY Mellon leverages Quantexa’s Network Analytics Platform and knowledge graph capability to continuously monitor data to detect changes and risk triggers within the customer’s network.
A firm’s competitive advantage is now largely determined by its ability to store complex datasets, access sophisticated insights from the data and make intelligent decisions based on both. As knowledge graphs continue to unlock opportunity across the financial sector, the level of impact on the business as a result of data informed decision-making will only become increasingly paramount. For this reason, the application of AI, machine and deep learning technologies to knowledge graphs and solving complex problems is the next frontier for any firm to sharpen its competitive edge and help drive efficiency in the finance industry.16
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