Graph analytics, a set of analytical techniques that look at relationships between different kinds of data, has the potential to drive innovation in the financial sector. Gartner included graph analytics in its “Top 10 Data and Analytics Technology Trends That Will Change Your Business” report. They cited its ability to enable discovery of new dimensions of the client relationship, business processes, the workforce, security and beyond.
What Is Graph Analytics and Why Does It Matter?
When data experts talk about “big data,” they’re only partly referring to the volume of data involved. The “big” also has a lot to do with how varied the data sets can be, and how complex the relationships can become between different entities portrayed in the data. The diversity of the data set can outstrip the ability of conventional, SQL-type analysis to make reveal what’s hidden within. Instead, using a graph database, the analytics process can show relationships between data that are often implied, but perhaps not overtly demonstrable using standard queries.
Visualization comprises the underpinnings of graph analytics. It’s often easier to see a connection between multiple data sets, or data streams, than it is to show them using analytical calculations. For example, in the financial field, it can be extraordinarily difficult to discover insider trading. It’s a classic “signal to noise” problem, where a suspicious trade can easily get lost in thousands of similar-looking transactions.
Using a graph-enabled semantic knowledge graph, compliance managers at financial firms can watch a continuously-updated visualization of trading patterns—complete with a number of simultaneous, seemingly unconnected data streams. To spot trading anomalies, the user can drag one trading metric inside of another. Or, he or she can remove part of a metric to recombine it with other datasets. Capabilities like this might highly a situation where a trader is “front running,” or trading on his account before he executes trades for clients. This can be almost impossible to spot using regular data reporting methods.
Gartner projects that graph processing and graph databases will grow at an annual rate of 100% through 2022. The financial world will almost certainly experience this kind of growth, given how promising the technology is for sector. Graph analytics give financial firms new powers in putting truly complex, diverse data sets to work.
2019 Market Guide for Data and Analytics by Gartner
Financial Industry Use Case Examples
Every area of the financial field, it seems, can benefit from the creation of rich semantic graphs foreseen by Gartner. The analysts offered compelling use cases for graph analytics in its report, several of which are relevant to finance. These include:
- Detecting financial fraud – in addition to catching suspicious trades, applied graph analytics can enable fraud analysts to spot anomalous behavior that’s suggestive of fraud. They might achieve this by linking graphs of network traffic with credit card transactions, for example.
- Optimizing customer engagements to determine success probabilities for the “next best offer,” e.g. mileage reward campaigns based on travel data gleaned from social graphs
- Balancing loads on digital networks and IT infrastructure to improve trading system performance
- Analyzing the behavior of bank employees to improve productivity or morale
Driving Innovation in Finance
If you’re in the financial field, you can use graph analytics to drive creative innovation in your business. Five approaches to graphical analysis lend themselves to this purpose.
- Leverage a social graph to see connections between people who are important to your financial business. Who is promoting your bank’s brand online? Who do they know? By analyzing the social graphs of your customers at scale, you may discover that a relatively small number of people are pushing your brand in a positive or negative direction. This kind of insight has long been promised from standard analytics, but it’s been difficult to realize given the scope and complexity of the data involved. Now, with advanced graph analytics, you can map social graphs that affect your business and use the resulting analysis to take action.
- Discover customers’ financial reasoning and motivation with an intent graph. Your customers frequently signal their intent, either to engage with your financial firm, do nothing or defect. The challenge has been reading intent into the vast, highly scattered data that characterizes intent. Graph analytics give you a practical tool for making these critical discoveries.
- Track your individual customers’ financial lives with a consumption (or payment) graph. Map their financial habits across different experiential venues like investing, borrowing, insurance, banking, credit card purchasing, rewards programs and so forth.
- Map your customers’ financial interests with an interest graph. This approach visualizes a person's interests, sometimes in combination with a social graph, e.g. how interested is a person in saving for retirement versus buying a second home? An interest graph might reveal hidden paths to getting a customer’s attention.
- Locate your customers with a mobile graph. Built from mobile device data, the mobile graphic shows where people are going and where they’ve been. This information can be extremely useful in determine where to locate bank branch locations or ATMs.
Graph analytics is becoming more common and influential, according to Gartner. This makes sense, given the insight-discovering power the technology conveys. Making graph analytics work in a financial firm requires a combination of tools and skills. Considering how graph analytics can drive creative innovation, it seems like a wise investment at this time.
Written by Tripp Smith
Data analytics thought leadership spanning entrepreneurial ventures and Fortune 500 companies centered around big data, technology vision, strategy, and product development. Chief Technology Officer at Clarity Insights focused on technology vision, product development, business development, partner alliances, coaching, and mentoring for the largest consultancy in the US focused solely on data analytics.
Topics: Customer Analytics