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
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.
There is great promise in new big data and machine learning technologies to enable lenders to tap into an ever-deepening pool of new data to analyze all aspects of risk and fraud. Identifying risk and fraud can require huge data volumes and large compute clusters which are typical of modern big data systems.