Big digital footprints can boost payment tech and fraud protection
Amazon, Apple, Facebook and Google already have a ton of data about consumers—searching, browsing and purchasing histories—and will build on that foundation to create hyper-personalized offers.
Aside from the customer personalization-related benefits, graph analytics provide fraud detection and prevention features -- an area top-of-mind for financial services organizations. This technology helps analyze, detect and visualize complex data patterns that indicate the potential for fraud. In fact, five of the 10 largest global banks and two of the world’s largest payment card companies have turned to advanced analytics in graph for their anti-fraud initiatives.
Graph analytics allow you to “drill down” into complex interrelationships among organizations, people and transactions. For example, a leading U.S. multinational investment bank can use advanced graph analytics to improve its fraud detection for debit and credit cards. The organization can add graph analytics to its machine learning system to find data connections between “known fraud” credit card applications and new applications. The bank can then identify more questionable patterns, expose fraud rings and shut down fraudulent cards faster. The result: millions of dollars saved annually.
Today’s financial services companies can neutralize competitive maneuvers with graph -- especially native parallel graphs. These graphs are built to understand, explore and analyze the complex relationships in financial services data, allowing data scientists and business users to go 10 or more levels deep into the data, across billions of payments and millions of consumers and businesses in real-time.
My bank already has a mountain of information about me, including over 23 years’ worth of data about the money earned, spent and saved over time, as well as multiple loans for buying homes and automobiles.
They could put together the most compelling offers for me to buy my next home, fund my next car purchase or suggest a compelling set of investment options that align with my financial life. Graph analytics empowers financial services organizations to uncover meaningful (and actionable) information from their data -- benefiting both the organization and customer.
Gartner Research predicts that the application of graph database and analytics will grow at 100% annually over the next few years to accelerate data preparation and enable more complex and adaptive data science.
Graph databases are purpose-built for storing and analyzing relationships among the data, as the data entities, as well as the relationships among them, are pre-connected and do not require time-consuming table joins or multiple scans across a large table.
The financial services industry can leverage graph to build real-time 360-degree customer views with accurate entity resolution now to leverage all of their internal data about customers, such as individuals, households and businesses, and marry that with external data sources to identify hyper-personalized next best actions. With years of actual financial behavior data, financial services companies have a phenomenal advantage over the newcomers -- they know what the consumers and businesses save, invest and spend and which offers are likely to be more successful in retaining and growing the customer base.
The ability to offer increasingly personalized recommendations can be a significant competitive differentiator. According to one study, 73% of customers prefer personalized shopping experiences. Moreover, relevant product recommendations can help financial services companies build deep relationships with their customers as they give them a sense of being understood and properly served.