Financials

Enabling real time fraud and risk analytics

In an industry where milliseconds matter and where insight directly equates to money, faster analytics offer a distinct competitive advantage. 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 truly real-time analytics demands including fraud analysis, risk management, algorithmic trading, and high-frequency trading.


Financial Fraud/Risk Analytics

Financial Fraud/Risk Analytics

A fraud risk assessment is an essential element of an organization’s fight against fraud. The topic of risk continues to be of critical importance across financial services segments. While there are many forms of risk, the most common form of risk across all financial segments is surrounding cybercrime and fraud. There is also a post-financial crisis regulatory aspect of risk management that forces lenders to know precisely how much capital they need in reserve. Keep too much and you tie up capital unnecessarily, lowering profit. Keep too little and you run afoul of Basel III regulations.


100Gbps Network DPI, Content Extraction

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, reroutes 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

100Gbps Network DPI, Content Extraction

MongoDB Acceleration using Grovf's MonetX Platform

MongoDB Acceleration using Grovf's MonetX Platform

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.