Arachnys Launches CRI Platform for Financial Investigations

London-based Arachnys has announced the launch of its Customer Risk Intelligence (CRI) platform. The cloud-based product encompasses anti-money laundering (AML), know-your-customer (KYC) investigative activities, and client onboarding processes.

The introduction of the platform has been driven by recent regulatory changes. “All the regulations that have come out over the past two years now require banks, when they perform risk assessments for specific individuals or companies seeking to open-up a business relationship, to go through a laundry list of checks,” says Ed Sander, president at Arachnys. “From determining the nature of the business, is it a natural form of business relations that those firms seem to have with the bank, to uncovering the hidden relationships and partnerships in determining whether or not there is a beneficial ownership status that needs to be reported.”

The vendor’s first customer for the platform was a tier-one institution headquartered in Southeast Asia. It rolled-out a portion of its intelligence platform to support 1,400 AML compliance analysts across five global locations. The rollout was completed in six weeks. Since then it has seen its usage grow within the global bank, with over 2,600 personnel now active on the platform. 

Sander talks about a shift away from straight rule-based prevention in the market to a focus on entity information and understanding the persona and profile of potential clients, looking to see if they have characteristics similar to individuals known to have perpetuated financial crime.

“We see a seismic shift in the market for how financial crime is prevented,” says Sander. “In the past, most banks invested huge sums of money into algorithmic applications that were designed to go out and find either known criminal activity, or things that were abnormal. This is a rules-based approach, an algorithmic approach to stopping and preventing financial crime. But here is the rub: No matter how expertly tailored your algorithms are, or how fast your robotic machine-learning analytic execution engine in the cloud is, it is only going to ever be able to report on things that have already happened, because you are looking at transactional history, or things that are already happening if there is real-time algorithmic capability.”