Investment Intelligence: Leveraging a Knowledge Graph to Create a Competitive Advantage

In the world of investment banking, knowledge is everything. Firms must prove to clients that they know the markets better than anyone else, that they have unique insights to share, and that they can make valuable and original recommendations. In this competitive environment an enterprise knowledge graph can serve as a secret weapon. In this article I will show how a knowledge graph can help organizations create trust in their expertise, deliver sharper analyses, and identify better opportunities.

Trust

The first requirement in any deal is trust. A client needs to have confidence that the investment firm knows the lay of the land and won’t lead them astray. During initial conversations with a potential or new client, an advisor only has a few opportunities to demonstrate their expertise. If they fail to make a good impression, the deal is off the table before it’s even begun.

A knowledge graph can provide support at this stage by arming the representatives speaking to leads with key talking points. Because a knowledge graph pulls together data from a variety of sources and a range of formats it can bring together information from news media, internal documents like emails or texts, along with traditional financial data subscriptions. That way, when an analyst queries the graph in preparation for an initial meeting, they can gather a complete picture of the company, its competitors, and the industry as a whole. The semantic links between these elements relate them in the graph even if the pieces come from wholly different contexts.

Insight

Once a client is on the hook and trusts that the firm has a general understanding of its industry, it’s time to demonstrate the ability to provide unique insights. Why should a client rely on your firm rather than any other that operates in the same space? Because your analyses provide a different perspective than everyone else’s. Given that most firms purchase data from the same providers, it can prove difficult to distinguish your insights. Again, a knowledge graph can help.

One of a knowledge graph’s superpowers is the ability to surface emergent information. While everyone can find the facts in a data set, a knowledge graph allows you to find the facts between data sets.

For example, say you have a subscription to a service that provides updates on European policy and another that includes the typical statistics and information about companies in a particular industry. Obviously, these sources do not natively speak to each other – but a knowledge graph can create synergy between them. Locations from the business data set such as factories or headquarters are linked with the abstract concept of those locales in the graph, as are policy documents that reference those places. In spite of their different origins and formats, the knowledge graph reflects the real-world relationship between the two sources.

A company might have the address 58 Industry Way, Dublin, Ireland. Even though nothing in the business data set references the European Union (EU), the graph will reflect that Ireland is in the EU and thus any new EU regulations would apply to this company. When a new EU statute goes into effect, a simple query makes it possible to immediately identify what companies will be affected. Other firms may have access to the same information, but they will lack the technological means to bring it together automatically.

Opportunity

Finally, once a firm has proven itself to a client, it must actually identify and present investment opportunities. Once more a knowledge graph comes in handy. A knowledge graph connects raw data with human concepts.

For example, an investment bank can within its knowledge graph rigorously define a “start-up.” This concept then exists distinct from any particular data source or table, and instead reflects agreed upon attributes that the firm has decided make a company a start-up. It can similarly define industries, regions, and business concepts. In fact, it can create different variations of those definitions for every one of its clients. When it comes time to identify potential targets for mergers and acquisition or other investments, the analyst at the firm can query the graph using the clients custom definitions to create a one-of-a-kind list of prospects.

Conclusion: Moving Beyond the Individual Contributor

Now, you may be thinking, “that’s great, but don’t investment banks already do all of these things without a knowledge graph?” And the answer of course is yes. But, to perform these tasks, firms rely on the human intelligence of individual contributors to pull the pieces together. When a major contributor leaves the firm, it’s a huge blow to the business as a whole because they brought unique expertise and knowledge that didn’t exist elsewhere in the business. A knowledge graph serves to document and automate that expertise.

Instead of relying on a few high-flyers, firms can leverage their abilities to empower others at the firm to be just as effective. Making human knowledge queryable is what a knowledge graph is all about. Its focus on concepts and relationships mimics the way people think, allowing it to capture the same nuance and connections that for most companies require a human being. This technological approach means firms can make those connections faster and more consistently than competitors relying on human insight alone, giving them a leg up against their competition.

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