Hatstand and Sqreem couples to deliver competency in Critical Growing Financial Crimes
Synechron’s services consultancy and capital Market Company, Hatstand announces a strategic partnership with a pattern detection and predictive analysis software firm named Sqreem.
Financial firms will benefit from a next generation surveillance platform that uses statistical (machine) learning and big data mining to help protect assets, client accounts and data from financial crime.
Sqreem leverages customers with security against financial crime through algorithms that can detect anomalies in patterns that can identify illicit behaviour and even predict future behaviour. The solution are touted to be used to detect a wide variety of financial crimes including: cyber fraud, money laundering, terrorist financing, rogue trading, bribery, corruption and market abuse.
On the other hand, Hatstand shares expertise in consultancy that works closely with capital markets institutions around the world on the most pressing business issues, including financial crime.
Both the companies couples to deliver the sales, implementation and client support around Sqreem’s software, working with firms to define the datasets to include, test and integrate the system, as well as train users. It will also help customise reporting or dashboards to reflect individual organisational needs.
“Global financial institutions face daunting and complex challenges in light of increasing fraud and regulation. They need to rely on technology to help pull together the disparate sources of data to identify aberrant behaviour,” said Ian Chapman-Banks, CEO of Sqreem. “By partnering with Hatstand and Synechron, we will be able to apply their extensive knowledge and expertise to leverage our software for the financial industry.”
Brad O’Brien, CEO at Hatstand comments, “Through this partnership, our capital market clients will benefit from truly innovative technology. We’re excited to pair our detailed knowledge services with Sqreem’s capability in machine learning and big data”.
