TigerGraph, the only scalable graph database for the enterprise, today announced that NewDay, a leading specialist financial services provider and one of the largest issuers of credit cards in the UK, will use TigerGraph’s advanced graph analytics to prevent and preempt financial fraud. NewDay, with TigerGraph, will transform how the company accesses and views potential customer data. NewDay specialists will now be empowered to identify and prevent fraudsters from joining their network by checking data against known and new fraud syndicates. NewDay, whose revenues exceed $1B, counts eight million customers on its growing roster, across some of the UK’s best-known credit cards and some of the largest online retailers.
“NewDay has always had a ‘customer-first’ mindset, and it is this dedication to empowering and protecting customers that fueled our signing on with TigerGraph,” said Danny Clark, head of fraud prevention, NewDay. “We had looked into other graph analytics companies after we upgraded our data platforms, yet none provided the forward-looking technology, ease of use, training or support that TigerGraph did. In our ever-changing world with increasingly interconnected data, we needed to uplevel our technology offering. At the same time, we wanted to enable our Fraud Investigation team to act autonomously – without relying on developers – to tune queries in near real-time with ‘train-of-thought’ analysis and speed.”
Financial services organizations are often a prime target for fraudsters and cybercriminals — and fraud numbers have escalated since the start of the COVID-19 pandemic. In fact, according to the LexisNexis Risk Solutions 2020 True Cost of Fraud Study, mid/large digital financial firms saw an increase of 39.48 percent in successful attacks since before the shutdown, while mid/large digital lending firms experienced a 27.56 percent increase. Fraud detection and prevention requires understanding connections and identifying anomalies in links among people, transactions, payment methods, locations, devices, times and more — and working with massive datasets to do this in real time.
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Forward-looking financial services organizations are turning to advanced analytics in graph, and applying it to connecting otherwise siloed datasets to stay one step ahead of fraud. Graph analytics allows you to “drill down” into complex interrelationships among organizations, people and transactions. One technique involves applying graph analytics to machine learning to find data connections between “known fraud” credit card applications and new applications. Organizations can then identify questionable patterns, expose fraud rings and shut down fraudulent credit card applications quickly. The result: Millions of dollars saved and – in NewDay’s case – an anticipated reduction of fraud across all its portfolios.
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