Getting sanctions screening right is a growing priority for financial services institutions in their AML/CFT compliance program. The constant drumbeat of increasing regulatory requirements, the mix of customer profiles that grows more complex by the day, and an ever-expanding menu of financial products and services have only made such due diligence all the more critical.

However, current AML/CFT detection systems and procedures that lean heavily on rules-based screening engines and manual inputs can not keep up with today’s reality. The issue of false positives is arguably one of the most consequential areas this plays out. On one hand, financial institutions need a new set of tools to accurately detect true matches and reduce the occurrence of false positives. On the other hand, too few false positives in an indication that an institution’s AML systems are not appropriately calibrated to catch the wily evaders.

What are False Positives and Why Do They Happen?

In its most basic form, an AML compliance program includes three fundamental functions:

1. Assess the risk of new and current customers
2. Implement screening measures to weed out clients who present a high risk for financial crimes
3. Conduct ongoing monitoring of customer activity

There are really two goals to an organization’s AML program. The first is to create an alert system that can efficiently identify and actively prevent the emergence of money laundering and other financial crimes within the organization. The second is to avoid regulatory sanctions and their fallout like the plague.

These two goals are often at odds with each other. Moving from financial crime identification to financial crime prevention can be a resource-intensive high-wire act.

In order to comply with the strict regulatory requirements and stiff fines for non-compliance, financial institutions will often operate on the side of caution in their screening and monitoring efforts.  That is, they will lean towards “Over-Screening“. In practice, this means the definition of suspicious activity, for example, would include a very broad range of actions and behaviors– each of which will generate an alert.

An AML alert does not necessarily mean that criminal activity of any kind is in fact happening. It is quite common for a subsequent manual review to result in a cleared warning. When this happens, it is called a false positive.

Such manual reviews are simply “baked” into the AML compliance system of many traditional and established financial institutions. The time and resources required to conduct this oversight is then written off as the cost of doing business.

The added piece of mind comes with a big price tag, however. The industry average rate of false positives currently hovers around 42%. But for larger institutions it can reach a whopping 95%. This translates into millions and even billions of dollars of lost revenue each year.

Another indirect cost of a high false positive rate is the negative impact it has on the customer experience. In recent years, the financial services industry has been required to pour more resources into the experience users have as they move through the buyer’s journey. According to the 2020 Digital Banking Report, customer experience is a key element in maintaining a competitive edge. Customers increasingly want financial institutions to provide personalized services. This is putting increasing pressure on financial institutions to perform to rapid, frictionless transactions and offer personalized customer communications.

A large number of false positives can slow down the on-boarding process as well as customer transaction times. They are thus an obvious stumbling block. However, compliance teams must find a way to reduce their rate of false positives without compromising the accuracy and effectiveness of AML transaction monitoring and screening.

What are Your False Positives Telling You?

The solution to the false positives conundrum is to fundamentally change the way your organization perceives false positives in the first place. False positives are a necessary and unavoidable occurrence. When your rate of false positives stays within a healthy range appropriate for your user base and risk profile, then it is a sign that your institution is on the right track.

When the number of false positives steps outside of that range, then you’ll want to pay attention. You are getting valuable information about your AML compliance efforts. Usually, it means that something within your system has to be tweaked or reworked.

Let’s consider a few examples…

Scanning sanctions watchlists and associated data, such as adverse media and PEP lists, coupled with ongoing transaction monitoring require firms to process and analyze a vast amount of information from a variety of sources which are themselves evolving. With sanctions screening in particular, these sources often vary wildly in quality and completeness, and they are bloated with redundant or useless information. On top of this, they generally come in an unstructured format.

A high rate of false positives could simply be the result of relying on poor data pools full of outdated or irrelevant information that has been ineffectively culled, organized and structured. Of course, the more confusing the data, the more challenging it becomes to discern false positives from true positive matches.

For this reason if your institution is experiencing an unusually high rate of false positives, you may want to reevaluate your data capture. You need to ensure it truly fits the needs of your organization along with the reality of the market base you are servicing.

For example, we developed VisionIQ, a comprehensive watchlist database with access to over 1000 watchlists and automatically updated news sources from more than 200 countries. VisionIQ is powered by advanced technologies, such as artificial intelligence (AI) and natural language processing (NLP). This smart data system is structured and organized to make it easy to selectively group watchlists for different risk profiles and screening criteria. Data is de-duplicated and cross-linked across watchlists to better resolve the data into personas.

Smart data by itself may help reduce the rate of false positives, however, it may not be enough. Part of the challenge is being able to manage the sheer volume and velocity of data creation today, as well as the constantly growing number of data sources. Even the best compilation of data and news is useless without an intelligent search and scoring engine built on AI algorithms and machine learning. Such an automated screening solution can significantly reduce labor-intensive manual interventions. In the process it will significantly minimize False Positives, while helping to ensure that True Positives are discovered.

Utilizing an automated screening solution solves yet another factor in a high false positives rate: the need for real-time data updates. It’s common for those involved in financial crimes to change their names or move to another region in order to avoid prosecution. It’s also common for innocent individuals to make such life changes.

Your organization should thus ensure that the customer data you are relying on is both relevant and up-to-date. A false positive might be created, for example, if a firm neglects to update a user’s change of name or list their new home address– particularly if the new region presents less of a risk than that of the previous residence.

When Even a Little Becomes A Lot

When it comes to false positives, some organizations, for example, a financial services business, may discover that their ability to conduct manual reviews on even a moderate rate of false positives is a struggle. Despite all the advances in technology, sanction screening still requires a significant amount of manual intervention that can dramatically slow down and disrupt the customer verification and on-boarding processes. Many sanction screening solutions on the market are designed to filter a set of watchlists. The search results must then be manually exported for review.

For nimble organizations with limited human resources, there are stand-alone decisioning systems that yield screening decisions in real-time without requiring manual review. This of course includes the automatic clearing of false positives using AI.

A smart, AI and NLP based sanction and PEP screening decision system, like our very own DecisionIQ, can render 95% of all verification decisions automatically within a 60-second response time and a low decision error rate. For records that still require manual screening, they can be sent within minutes to compliance officers, who should have all the info they need to take immediate action.  The screening decisions are then setup for ongoing monitoring based on your risk-based approach.

Slow customer on-boarding and the constant drumbeat of false positives will quickly eat up your profitability and your competitive advantage. On the other hand, failing to identify criminal financial activity can lead to unwanted regulatory attention and action.

The use of smart automated technologies can help to alleviate the challenge of false positives in sanction screening more effectively, more rapidly, and more affordably than ever before.

Adverse media screening, also known as negative news screening, is an integral part of your KYC and AML regime. In its most basic form, AMS is the process by which a current or prospective customer is screened for negative news mentions and other unfavorable information. AMS allows your organization to flag a person or business as a potential risk and thus avoid future reputational and compliance damages.

Getting AMS right, however, comes with many challenges, including:

• AML compliance teams are often already bogged down by time consuming, manual processes. Manually searching news sources via search engines just adds to the complexity.
• The sheer volume and complexity of the news data that must be scanned and sorted is staggering.
• Dealing with news feeds versus public watchlists is exponentially more complex. It goes beyond filtering false positives and identifying the correct person or business. One has to resolve the news article for sentiment, context, and other aspects of relevancy to determine if it is truly adverse media.
• AML compliance reporting requirements are becoming more stringent among smaller financial institutions and MSBs. Credit unions in particular are currently facing many changes to their AML compliance requirements. This leads to either under-reporting or over-engineering, either of which can lead to costly mistakes.

Institutions need to develop AML compliance systems that can successfully and efficiently identify high-risk customers. In particular they should define:

• When to perform adverse media scans?
• What types of data to search for and from which sources?
• What type of systems and procedures to use in order to balance manual and automated interventions?

Adverse Media Screening: Finding the Right Balance

Luckily, AMS is not rocket science. However, developing an effective and efficient AMS does require a structured approach as well as the integration and implementation of some core technologies. It also requires striking a balance between taking a risk-based approach to AMS while maintaining high-level of customer service and flexibility to scale operations for future growth. Done right, and done with the right technologies, such as AI and NLP, AMS should not result in a slow down of your customer onboarding and ongoing monitoring.

That said, here are four best practices that your organization can use to get your AMS off to the right start:

1. Determine Your Risk Tolerance and Perform a Risk Assessment

Setting the parameters for what constitutes an acceptable level risk when deciding whether or not to work with a particular customer, is a critical first step. This process involves answering a number of questions, such as:

• Which high-risk groups of individuals or businesses is your institution comfortable servicing?
• Which offenses would bar a potential customer from being accepted? What offenses would not?
• How much weight do charges and allegations get versus an actual conviction by a court or an admission of guilt?
• Which types of adverse media could be considered lower-risk over time?
• What systems and processes will be in place to take action and perform Enhanced Due Diligence (EDD) when the results of adverse media screening return matches?

Every financial institution and MSB has its own unique exposure to potential financial crime based on the kinds of customers it serves, the locations it operates in, it’s product and service mix, and the channels it uses to service customers. Every financial institution and MSB also has its own unique risk tolerance.

Before you can even begin to design an effective AMS, it is crucial that you set clear risk categorization thresholds and outline the relevant actions and details required for each.

To help with you with that, try our FREE AML Risk Assessment Tool!

2. Decide When is the Right Time to Screen

Though most financial institutions and MSBs engage in customer screening during the onboarding process, many do not have a clear and consistent screening strategy, particularly when it comes to ongoing monitoring for adverse media. When standardized procedures and decisioning are lacking, both routine monitoring as well as monitoring triggered by an event, such as a news item, can result in discrepant responses.

Adding fuel to the fire are increases in regulatory expectations, expanding sanctions data bases and watchlists, and an increasing number of categories that must be screened against. For example, in recent years AML transaction monitoring has grown to include crimes dealing with human trafficking and smuggling, as well as new business customer types that fall in ‘High Risk’ category, such as cannabis-based businesses and a wide range of innovative fintechs, such as crypto-currency.

Some of the best practices to help ensure that your organization screens customers at the right time without increasing the rate of false positives includes:

• Decide which events and activities warrant further examination
• Include AMS in the standard onboarding procedures for all new customers and for any new accounts from current customers
• Establish automated AMS for all customers on a routine basis
• Screen current higher-risk customers based on your organization’s risk-based approach.

3. Use Credible and Current News Sources

These days, anyone with internet access can become a publisher of “news.” Although this helps to ensure that critical information becomes available to the public, at the same time it creates several challenges for your AML compliance system and procedures:

• First, how can you verify the credibility of an original adverse media source?
• How should you handle alternative news sources, such as social media?
• Which of the many tens of thousands of verifiable sources should you choose to monitor for a particular customer– especially since the sources themselves may change?
• How do you ensure that you are working with current news items?
• How far back in time to go for screening adverse media?
• Finally, how can you sift through all of this information in an efficient and effective way?

Given the dynamic nature of sanctions and screening data, you need a smart watchlist database that can actively weed out unnecessary information while focusing in on items of true value. This data can then be coupled with a dynamic, automated system to help you match, sort and analyze the results. You must get this step right before you even venture into the minefield of negative news screening.

That brings us to final best practice in adverse media screening…

4. Use Smart AMS Technologies

Automated AMS technology can help your organization address the deluge of information and alerts by focusing in on the most relevant matches. Sophisticated AMS tools also significantly reduce false positives and help you screen customers according to their risk categories.

Consider our Adverse Media Check ^®. We harness the power of AI, NLP, and machine learning to continuously monitor thousands of quality relevant global news sources. Our sophisticated algorithms get to work identifying sentiment, context, and the prime actors (i.e. personas) in each article. This information is then structured and fed into a persona database of individuals, companies, and politically exposed persons (PEPs) to identify cross-links between news and government and public watchlists. Each Persona is also tagged with FATF supported categorization of risk of financial crime.

Adverse Media Check also feeds into the KYC2020 DecisionIQ decisioning engine to render Pass/Fail/Verify decisions on AMS so that you can speed up your customer onboarding with the help of AI-based decision systems.

When an auditor flags a MSB or FinTech for not doing enough to fight AML/CTF crime, the impulse response is to ‘Select All’ and screen every list available to them indiscriminately. In other words, they go from screening just one list (most likely OFAC) to “over-screening.”

At KYC2020, we could have invested in writing a blog and talked about why “over-screening” is an expensive problem. We could have dug into how over-screening results in excessive false positives. Into how these false-positives require manual effort to clear and cause unnecessary cost and friction that slows down customer onboarding. And we could have told you how our paid services could help.

Instead, we decided to have our CAMS AML specialists build a tool to help you identify what Sanction, PEP, and Watchlists you should be screening based on your company’s risk profile. And we decided to make our watchlist builder available free of charge. Of course, we have the standard disclaimers, and you should already know that a tool cannot replace the advice of your Compliance Officer.

The tool gives you an effortless way to check to see what sanction and PEP lists we think you should be screening and whether you are doing enough, or too much based on your AML Risk Profile. 

It’s a 5 minute investment of your time.  Get started by clicking below.

So what’s the catch?  Nothing! The tool is no obligation and we do not take any payment information. We do hope you might take the time to see what awesome and affordable AML compliance solutions we offer, from Global Watchlist Data to Sanction Screening to AML compliance consulting services. Chat with us online now, email us, or just use the tool. Our aim is to put an end to over-screening and reduce the cost and friction of AML compliance.

And oh by the way, if you want to learn more about ‘Over-Screening’ and other AML compliance challenges faced by Non-Bank Financial Firms and FinTech Payments Innovators, read our Whitepaper.  It’s long and possibly dry, but it may help you save some money, reduce compliance friction, and improve the customer experience.