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 sector faces 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 the peril of big data; this represents both a significant hurdle and an unprecedented opportunity for banks to rethink how they can use real-time data analytics to gain a unified view of their customers. This information, in turn, helps the bank make smarter, data-driven decisions about the business. Banks are under even greater pressure these days as a legion of cloud-focused fintech start-ups have set their sights on customers who have come to expect the same real-time convenience from their banks. they find elsewhere in their digital lives. But getting there will require a new approach to how 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 a fundamental tool for storing, managing, and analyzing data. However, despite their name, relational databases do not store relationships between data items and do not scale particularly well when you need to perform operations on different fields. The rigid structure of these systems was never designed to provide the agile 360 degree vision that today’s financial institution needs.
This becomes evident when organizations seek to incorporate structured and unstructured datasets into their analytical models. Unstructured data – which can include anything from notes in a claim to call center interactions – exists in multiple sources and in growing volumes. The opportunity to exploit these sources of information is attractive, but elusive.
It’s like finding a huge deposit of valuable minerals only to learn it’s way too deep to mine profitably. As a result, these legacy database systems get bogged down when trying to incorporate unstructured data into their models. Second, these rich data sources often remain siloed and out of reach.
There is also the question of data collection and storage. Although financial services institutions are constantly ingesting vast amounts of customer data across a wide range of sources – from transaction data and credit scores to financial records and statements – they are too often limited by how they can implement them.
Why the future will be drawn
While relational databases require a defined structure, graph databases organize 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 commonalities that can help reveal latent relationships and subtle patterns. Adoption of graphing technology is expected to skyrocket due to the need to ask complex questions across large and disparate data sets. According to Gartner, “by 2025, graphics 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 graphical technologies, it becomes possible to trace the flow of data and visualize the dependencies that exist between different tables of data. More importantly, these relationships can be visualized together in a single, holistic, connected data map. This kind of end-to-end visibility allows you to analyze and understand exactly what happens (or predicts what will happen) if a change or issue occurs elsewhere in the data landscape.
Three-way graph databases enable real-time decision making
Graph databases are already used by some of the biggest banks in the world. While there are dozens of potential use cases, here are three of the most compelling scenarios that demonstrate how graph databases enable real-time operational decision making in banking today.
- Real-time fraud detection: Fraud analytics solutions that rely on first-generation relational database systems simply cannot analyze datasets at the scale required to flag fraudulent transactions in real time. Clients expect abnormal transactions to be reported in near real time. However, banks must walk a fine line so that frustrating false positive notifications are not triggered unnecessarily.
By supplementing chart analysis with machine learning systems, financial companies can uncover data connections between existing “known fraudulent” credit card applications and new applications. This allows them to identify hard-to-spot patterns, expose fraud rings, and close fraudulent cards quickly.
- Improved AML compliance: The practice of Know Your Customer (KYC) has become fundamental for 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, the graph must seamlessly collect, analyze, and correlate deep-layered data to reveal complex relationships between individuals, organizations, and transactions. This is how financial services organizations unmask criminal activity and comply with changing federal regulations.
- Dynamic credit risk assessment: With an estimated 26 million consumers untracked by FICO and other credit bureaus, risk assessment and monitoring has only become more difficult. Determining whether a customer qualifies for a loan, mortgage or line of credit presents both risks and opportunities for financial institutions. These organizations must leverage all the data at their disposal to make an informed, real-time decision about a customer’s creditworthiness in real time or risk losing market share. It also requires the ability to pull data from a variety of disparate third-party sources, normalize the data so it can be analyzed quickly, and do so at a scale that doesn’t hamper network performance.
The explosive volume and speed of data and the need for real-time decision making have transformed modern banking. Advanced graph analytics enables deeper insights, complementing existing BI technology and powering the next generation of artificial intelligence and machine learning applications. Banks and financial institutions that are able to secure the data advantage today will be best positioned to thrive tomorrow.
About the Author: Harry Powell is Head of Industry Solutions at TigerGraph, provider of a leading graph analytics platform. In this role, he leads a team of both industry subject matter experts and seasoned analytics professionals focused on the key business drivers impacting forward-thinking businesses as they operate. in a digital and connected world. A graphics technology veteran with over 10 years of industry experience, he has spent the past four years leading the data and analytics business at Jaguar Land Rover, where the team generated $800 million dollars in profits over four years. At JLR he was an early adopter of TigerGraph, using a graph database to solve supply chain, manufacturing and purchasing issues during the height of the Covid shutdown and the shortage of semiconductors. Prior to that, he was Director of Advanced Analytics at Barclays. His team at Barclays has created a number of graphical applications and brought world-class data science innovations to production, including the first Apache Spark application in the European financial services industry.
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