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“Any sufficiently advanced technology is indistinguishable from magic,” wrote 2001: A Space Odyssey author Arthur C. Clarke. At WorkFusion, we frequently hear, “That sounds like magic!” — especially when we talk to customers about our Adverse Media Screening use case, built on top of our Intelligent Automation software.
What is so magical about WorkFusion’s solution, and how do anti-money laundering (AML) leaders bring it into their organizations? The answer starts with understanding what is negative news screening, what this solution can do, specific details of how it is applied, and seeing successes already underway.
A Magic Solution to Adverse Media Monitoring
Adverse Media Screening — also called Adverse Media Monitoring or Negative News Search — is a common AML check that sounds simple enough: An analyst searches for news articles about a person or company, reads each of them for indications of suspicious activity, and creates a report if there appear to be any concerns, escalating as needed. Each of these manual reviews can take 20–30 minutes per search.
Though seemingly straightforward, this process, required for financial institutions by regulatory agencies, can pose a large operational challenge. Depending on the size of the institution, the need for frequent and ongoing searches of enormous volumes of content (the bulk of which is irrelevant) can cost millions annually.
WorkFusion technology, or “magic,” if you will, automatically finds and reads these articles, assesses the adverse context of keywords as they relate to the subject of the search, spots risk factors like mentions of rogue nations or politically exposed persons (PEPs), and prioritizes the relevance of each article. Our solution optimizes a 20-minute process down to 2 minutes, helping companies cut through the request queue while assisting the team with stronger results and a detailed audit trail.
Sure, a 10x productivity improvement sounds magical, but what does it look like?
3 tricks for working with Adverse Media Screening tools
There are a handful of scenarios — or, to stick with the theme, magic tricks — that are part of WorkFusion’s solution. Here are three examples that one of our customers recently worked through.
Magic Trick #1: Prioritizing Deeper Content
A search was required for an individual with a common name. Not quite as common as “James Smith” or “Maria Garcia,” but one where there were multiple instances of criminal activity from various same-named people, causing an excessive number of false-positive results.
WorkFusion’s software was able to locate the most relevant piece of content on the fourth page of search results and prioritize it due to its AML context. If this search had been done manually, there would have been considerable wasted time and effort. Perhaps it would have been completely missed, as many companies limit their analysts to the first 20 individual articles returned. With automated search analysis, coverage of results increased, ensuring key content is uncovered and reviewed.
Magic Trick #2: De-Prioritizing Allegations
A search was requested for a medical device manufacturer, the defendant in a class-action lawsuit. The case did not end with a conviction, but considerable news content leading up to that conclusion was focused on the allegations and trial, thereby muddying the search results with negative content for the device manufacturer.
WorkFusion’s software was able to separate out articles focused on the allegations and prioritize content focusing on the end result — whereas a manual search would have entailed sifting through a lot of distracting and irrelevant reports.
Magic Trick #3: Learning as Laws Adjust
When another search was requested regarding an individual who took part in the sale of synthetic marijuana products, the software needed to understand that jurisdictions differ and whether abbreviations like CBD and THC indicated illicit activity — which depends on evolving laws and overall context.
While WorkFusion’s software did find and analyze articles, some were given lower relevance due to current understandings from available data. This represents a key part of the solution: the ability to learn as analysts provide feedback, especially in ever-evolving situations like compliance laws and regulations.
These magic tricks, among others, allow AML analysts to increase coverage, ensure accuracy and make improvements over time.
See how WorkFusion’s solution for Adverse Media Monitoring works on practice in this demo video from our recent AML Cost Takeout Summit.
So is there really room for magic within AML regulation?
Obviously, WorkFusion isn’t trying to sell tricks, but rather, our Intelligent Automation software. While it may seem like magic, this really is a practical automation approach with the most useful machine learning, natural language processing (NLP), and human-in-the-loop (HITL) techniques available.
Customers interested in an adverse media screening tool typically care more about PEPs than NLP and more about regulators, compliance and proven results than feature engineering, training sets, and F1 scores. So, let’s focus on the business context (although please request a demo to dive deeper).
Regulators themselves are actually encouraging financial institutions to integrate innovative approaches into their processes. In a December 2018 joint statement, federal banking agencies and the Treasury’s FinCEN specifically welcomed AI to “strengthen…compliance approaches… [to] … maximize utilization of banks’ BSA / AML compliance resources.” Regulators are interested in enhanced processes, opening the way for compliance teams to approve solutions like WorkFusion’s that augment the workforce towards that maximized utility.
Prospective customers would not be the first to start waving their magic wands to automate Adverse News Monitoring with WorkFusion. To name two of our AML customers that are already achieving value:
- Scotiabank in Canada is excited for its ability to achieve a huge return on investment with a more compliant and efficient process. Rather than eliminating headcount, Scotiabank is retaining 350 well-trained people who now spend significantly less time reviewing articles and can concentrate on more meaningful work. The numbers: a 95% increase in efficiency at an impressive $15 million annual savings.
- Carter Bank in western Virginia is another success story. They were unable to appropriately handle reviews for medium-risk customers due to capacity constraints and 80% turnover. With WorkFusion’s Negative News ongoing monitoring, they were able to drastically increase capacity and efficiency, quickly achieving the high value of Negative News and extending to over 30 other use cases. Their numbers: 90% increase in efficiency and a $2.5 million reduction in operating efficiencies.
And that’s just a subset. Many more are well on their automation journeys but not yet ready to talk publicly of their successes. Expect to hear more as we’re able to share!
Need better Adverse News Monitoring? Let’s chat.
If WorkFusion’s Adverse Media Monitoring use case is appealing to your magical fantasies or just your practical realities, we’d love for you to contact us.
You can learn more on our Use Case Navigator, but we’d love it more if we could have a conversation. We want to not only show you but prove how the magic tricks apply to your data, enabling you to be an innovator who starts saving your company the millions that other banks are already achieving.
