Financial compliance analysts will now have a more streamlined experience generating discrete profiles from watchlists, sanctions, adverse media data, and PEPs (politically exposed persons), leveraging AI to tackle the large volumes of alerts they have to review.

This game-changing development uses sophisticated machine learning, natural language processing and graph analytics to generate person and company-specific risk profiles. It reviews all relevant data from both structured and unstructured sources to build discrete profiles for individuals and organisations for significantly improved accuracy, effectiveness and efficiency in the fight against financial crime.

Testing of the solution has shown that there can be as much as a 91% reduction in false positives whilst benefiting from a 5% improvement in valid matches found.

Pressures are mounting on financial compliance teams

According to Thomson Reuters, while 74% of financial services companies expect their regulatory burden to increase in the next year, 61% believe their teams will not grow in size, as recruitment needs are called into question. Ultimately, financial compliance teams will be forced to pick up more work without the necessary headcount. Having the right technology to support those extra work loads will be critical.

Identifying risk in a customer portfolio remains a huge challenge thanks to limited and often problematic customer and media data. Many screening methods still rely heavily on manual and time consuming processes, which can generate a large number of false positives, struggle to achieve accuracy at scale, and put a significant time burden on analysts.

Ripjar’s AI Risk Profiles uses AI-powered multi-lingual name matching and entity resolution to overcome those screening challenges such as common or high profile names. The technology automatically separates out the matches into distinct profiles, so analysts can quickly assess if the risky person or company in the news or on a sanctions list is their new or existing customer. Importantly, once an analyst has marked a specific profile as not being relevant, new alerts will not be generated unless there is a significant change to the client match, eliminating operational costs.

This latest evolution of Labyrinth Screening, AI Risk Profiles, captures a large number of secondary identifiers - such as dates of birth, nationalities, locations and roles - from unstructured text. This expansion of context leads to richer data and better recall. Standard watchlists are also enriched with these additional properties, improving sanctions and PEP screening accuracy. 80% of profiles now contain secondary identifiers, which is key to reducing false positives.

Jeremy Annis, CEO at Ripjar: “Financial institutions are coming under increasing cost and time pressures when it comes to compliance and regulation when screening their clients. Ripjar’s new AI Risk Profiles solution within the Labyrinth Screening Platform offers a much faster, more efficient option for financial compliance analysts. They have been shown to decrease analyst workloads by up to 10x, resulting in less operational overhead alongside improved accuracy. With AI, analysts can be confident in the profiles they screen and be a reliable source for tackling financial crime.”

“Ripjar’s innovative approach to solving real market challenges, based on advanced technology and analytics capabilities, makes it an exciting player in the name screening space,” said Nick Vitchev, Research Director at Chartis.