{"id":397363,"date":"2020-12-10T09:03:09","date_gmt":"2020-12-10T14:03:09","guid":{"rendered":"http:\/\/www.marketnewsdesk.com\/?p=397363"},"modified":"2020-12-10T09:03:09","modified_gmt":"2020-12-10T14:03:09","slug":"newday-scores-with-tigergraph-cloud-to-fight-financial-fraud","status":"publish","type":"post","link":"https:\/\/www.marketnewsdesk.com\/index.php\/newday-scores-with-tigergraph-cloud-to-fight-financial-fraud\/","title":{"rendered":"NewDay Scores with TigerGraph Cloud to Fight Financial Fraud"},"content":{"rendered":"

\nLeading UK Credit Card Consumer Finance Company Uses Advanced Graph Analytics to Intercept Fraudulent Credit Card Applications, Boost Anti-Fraud Efforts
\n<\/h2>\n
\n

REDWOOD CITY, Calif., Dec. 10, 2020 (GLOBE NEWSWIRE) — TigerGraph<\/u><\/a>, the only scalable graph database for the enterprise, today announced that NewDay<\/u><\/a>, a leading specialist financial services provider and one of the largest issuers of credit cards in the UK, will use TigerGraph\u2019s 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\u2019s best-known credit cards and some of the largest online retailers.<\/p>\n

\u201cNewDay has always had a \u2018customer-first\u2019 mindset, and it is this dedication to empowering and protecting customers that fueled our signing on with TigerGraph,\u201d said Danny Clark, head of fraud prevention, NewDay. \u201cWe 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 \u2013 without relying on developers \u2013 to tune queries in near real-time with \u2018train-of-thought\u2019 analysis and speed.\u201d<\/p>\n

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<\/u><\/a>, 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 \u2014 and working with massive datasets to do this in real time. 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 \u201cdrill down\u201d into complex interrelationships among organizations, people and transactions. One technique involves applying graph analytics to machine learning to find data connections between \u201cknown fraud\u201d 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 \u2013 in NewDay\u2019s case \u2013 an anticipated reduction of fraud across all its portfolios.<\/p>\n

\u201cNewDay works with millions of customers, each with billions of rows of valuable account data that we can use to disrupt criminals. Traditional relational databases could not scale to analyze the volume of interconnected data or any potential connection to organized crime that we wanted to find,\u201d said Jamie Burns, senior fraud strategy and analytics manager, NewDay. \u201cOur recent developments with Python and AWS have allowed our fraud prevention team to really utilize these new data science tools to truly take the lead in the fraud prevention space.\u201d<\/p>\n

The investigations and fraud prevention team needed the ability to view customers\u2019 online behavior in a simple, real-time interface; this would help specialists guide customers to make better credit decisions while checking for potential fraud. Enter TigerGraph.<\/p>\n

\u201cNewDay\u2019s teaming with TigerGraph further validates our strength in the financial services fraud detection and prevention sector,\u201d said Martin Darling, general manager for EMEA at TigerGraph.\u201cWe have worked to deliver meaningful data insights with graph \u2013 insights that translate to measurable business impact. NewDay has a strong footprint in the subprime credit card market, and with that comes increased fraud risk. Powered by TigerGraph\u2019s advanced graph analytics, NewDay can now uncover and prevent fraudsters from joining their credit card network immediately \u2013 and without development team involvement. That means fraud detection and customer protection are immediate and preemptive.\u201d<\/p>\n

NewDay selected TigerGraph for its simple implementation and ease-of-use. TigerGraph GraphStudio integrates all phases of graph data analytics into one graphical user interface, providing a single customer view available to operational, technical and business stakeholders.\u00a0NewDay will also use TigerGraph Cloud, the industry\u2019s first and only distributed native graph database-as-a-service that helps companies quickly and easily build and run applications that work with highly connected and complex datasets. TigerGraph Cloud enables teams to use the cloud vendor of their choice, including support for Amazon Web Services (AWS). NewDay will run TigerGraph Cloud with the AWS virtual machine configuration. NewDay will next add TigerGraph to its real-time transactional fraud detection efforts as well as to its call center and anti-money laundering (AML) division.<\/p>\n

\n Helpful Links<\/strong>\n <\/p>\n