Financial services belong to the industry where milliseconds matter, where insight directly equates to money and faster analytics itself represents a distinct competitive advantage.
While the structured customer information is increasing both in size and scope, this is still the world of unstructured data that evolves as a more important and wider source for customer insight.
While dealing with a huge amount of data in a daily manner, banking and financial companies aren’t necessarily aware of how to maximize their potential since the data is unstructured or acquired out of the firm.
To leverage these significant analytical opportunities and overcome the obstacles that their data analysts/scientists encounter, new approaches are needed.
Grovf makes it possible for financial organizations to derive insights and make predictions from vast volumes of complex and streaming data in milliseconds. Use Grovf real-time analytics IPs to optimize your Big Data analytics and decision-making on uses like risk management, better customer service and targeting, fraud prevention, algorithmic trading, high-frequency trading, etc.
Learn more about GRegex IP.
Through our financial data analytics acceleration, time resources maximization and cost-efficiency, Grovf helps financial sectors data engineers achieve incomparably better performance. As a result, the client companies make informed decisions, gain more predictable risk outcomes, save time, and establish a fraud-less trusted environment.
Deep packet inspection (DPI) is an advanced method of examining and managing network traffic. It is a form of packet filtering that locates, identifies, classifies, routes, or blocks packets with specific data or code payloads that conventional packet filtering, which examines only packet headers, cannot detect. DPI combines the functionality of an intrusion detection system (IDS) and an Intrusion prevention system (IPS) with a traditional stateful firewall. This combination makes it possible to detect certain attacks that neither the IDS/IPS nor the stateful firewall can catch on their own. Stateful firewalls, while able to see the beginning and end of a packet flow, cannot catch events on their own that would be out of bounds for a particular application
Databases provide a wealth of functionality to a wide range of applications. Yet, there are tasks for which they are less than optimal, for instance when processing becomes more complex or the data is less structured. As data is exploding exponentially only CPU based systems no longer provide real-time insights to businesses in a cost-effective way. At Grovf we designed a Monet – A FPGA based smart memory extension for near memory data processing. Monet implemented on top of Xilinx’s Alveo U50 acceleration card and once plugged into server’s PCIe bus acts as a standard RAM memory for the Linux operating system with in-memory compute API capability.