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For today’s modern bank, the ability to access and analyze data in real time is almost as important as its access to capital. However, the banking industry is facing a big “big data” problem: an enormous amount of valuable data is spread across disparate sources, formats, and geographic locations.

This is the promise and peril of big data; it represents both a daunting barrier as well as an unprecedented opportunity for banks to rethink how they can use real-time data analytics to gain a unified view of their customers. These data insights, in turn, help the bank make smarter, data-driven decisions about the business. Banks are under even greater pressure these days as a legion of cloud-first, fintech upstarts have set their sights on their customers who have come to expect the same real-time convenience from their banks that they find elsewhere in their digital lives. But getting there will require a new approach to the way data is collected, managed, and processed.

An Oxymoron: Relational Databases Don’t Store Relationships

The journey to real-time data operations begins with the humble database. For the past few decades, relational databases have served as the foundational tool for data storage, management, and analysis. However, despite their name, relational databases do not store relationships between data elements nor do they scale particularly well when you have to perform operations across different fields. The rigid structure of these systems was never designed to deliver the agile, 360-degree view that today’s financial institution requires.

This becomes evident as organizations look to incorporate both structured and unstructured data sets into their analytical models. Unstructured data – which might include anything from notes in a claim to call center interactions – exists across multiple sources and in increasing volumes. The opportunity to mine these sources for intelligence is enticing, yet hard to attain.

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It’s like finding a huge deposit of valuable minerals only to learn that it’s far too deep to be mined in a cost-effective manner. As a result, these legacy database systems get bogged down when trying to incorporate unstructured data into their models. Then these rich data sources often remain siloed and just out of reach.

There is also the issue of data collection and storage. Although financial service institutions are continuously ingesting copious amounts of customer data across a broad spectrum of sources – from transaction data and credit scores to ledgers and financial statements – they’re all too often constrained by how they can put it to work.

Why the Future Will Be Graphed

Whereas relational databases require a defined structure, graph databases organize themselves around relationships rather than forcing data into strict frameworks. They connect the dots or “nodes” across a wide variety of data types, formats, categories and systems, finding the commonalities that can help reveal latent relationships and subtle patterns.  Adoption of graph technology is expected to skyrocket due to the need to ask complex questions across large and disparate data sets. According to Gartner, “by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the organization.” With modern graph technologies, it becomes possible to chart the flow of data and visualize the dependencies that exist between different data tables. More critically, these relationships can be viewed together in a single holistic, connected data map. This type of end-to-end visibility allows you to analyze and understand exactly what is happening — or predict what will happen — should a change or problem arise elsewhere in the data landscape.

Three Ways Graph Databases Enable Real-time Decision Making

Graph databases are already being put to use by some of the largest banks around the world. While there are dozens of potential use cases, what follows are three of the more compelling scenarios that demonstrate how graph databases are enabling real-time operational decision making in the banking industry today.

1. Real-time Fraud Detection: Fraud analysis solutions that rely on first-generation relational database systems are simply not able to analyze data sets at the scale required to flag fraudulent transactions in real time. Customers have come to expect that anomalous transactions be flagged in near real time. However, banks must walk a fine line so that frustrating false positive notifications are not needlessly triggered.
   

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   By supplementing graph analytics with machine learning systems, financial firms can uncover data connections between existing “known fraud” credit card applications and new applications. This enables them to identify hard-to-spot patterns, expose fraud rings, and shut down fraudulent cards quickly.

2. Improved AML Compliance: The practice of Know Your Customer (KYC) has become fundamental to banks and their ability to comply with complex anti-money laundering (AML) regulations and governance requirements. Perhaps no other banking use case requires more data-intensive pattern matching than an AML capability. Here, graph must seamlessly collect, analyze, and correlate layers-deep data to reveal complex relationships between individuals, organizations, and transactions. This is how financial services organizations unmask criminal activity and comply with evolving federal regulations.

3. Dynamic Credit Risk Assessment: With an estimated 26 million consumers not being tracked by FICO and other credit bureaus, risk assessment and monitoring have only grown more challenging. Determining whether a customer is qualified for a loan, a mortgage, or line of credit presents both risks and opportunities for financial institutions. These organizations must leverage all data at their disposal to make an informed, real-time decision regarding a customer’s creditworthiness in real time or risk losing market share. It also requires the ability to cull data from a variety of disparate third-party sources, normalize the data so it can be quickly analyzed, and do so at a scale that doesn’t impede network performance.

The explosive volume and velocity of data along with the need to render real-time decisions has transformed the modern banking industry. Advanced graph analytics enables deeper insights, complementing existing BI technology and powering the next generation of artificial intelligence and machine learning applications. The banks and financial institutions who are able to secure a data advantage today will be the ones best positioned to thrive tomorrow.

About the author: Harry Powell is Head of Industry Solutions of TigerGraph, provider of a leading graph analytics platform. In this position, he leads a team comprosed​ of​ both industry subject-matter experts and senior analytics professionals focused on key business drivers impacting forward-thinking companies as they operate in a digital and connected world. A graph technology veteran, with over 10 years industry experience, he spent the past four years running the data and analytics business at Jaguar Land Rover where the team contributed $800 million profit over four years. At JLR he was an early adopter of TigerGraph, using a graph database to solve supply chain, manufacturing and purchasing challenges at the height of the Covid shutdown and the semiconductor shortage. Prior to that he was the Director of Advanced Analytics at Barclays. His team at Barclays built a number of graph applications and released world-class data science innovations to production, including the first Apache Spark application in the European financial services industry.

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