For SME’s, credit unions, and Fintech companies, finding efficient and affordable solutions had been out of reach… until now.  RegTech innovators are taking advantage of AI, ML (machine learning), and NLP (natural language processing) to deliver compliant screening solutions that are fast, affordable, and low friction for smaller businesses.

The False Positives Challenge

There are a few factors that slow down the timing of approvals during screening. A main one is the volume of false positives exposed. This is a nightmare for companies looking to improve their customer acquisition. They win customers by advertising faster approvals in opening accounts and managing transactions.

When the volume of false positives spirals out of control, unexpected delays extend from a few days to weeks. Moreover, delays in customer onboarding and transaction processing do not just cause frustration for the customer but also for the company.

Online customers are not sticky, and according to a study done by Forbes, customer experience is perhaps the biggest reason why customers switch vendors.

Machine learning and AI

With AI and machine learning becoming an essential part of the compliance world. Companies can now manage the volume of false positives with autonomous clearing of alerts. AI based decision systems use a rule-based approach and built-in learning models to filter out the data.

Once the AI learns of a particular alert that is cleared manually, it will learn the behavior and create new rules to clear false positives automatically.

Decision systems for automated clearing of false positives can be a business savior. However, replacing human intelligence with artificial intelligence does not come without risks. You need to follow best practices to avoid auditor scrutiny and avoid penalties for non-compliance.

For example, one of the system’s most important features is that it must be auditable and provide transparent proof of decision that is easy to read and track. For more on best practices for AI-based decision systems, download this whitepaper from KYC2020.

An ongoing process

Ongoing monitoring of customers is a requirement for most AML compliance regimes since databases and sanction lists update almost daily.

Algorithms are always learning new alerts and rules. Manual processes are no match against AI-based systems for ongoing monitoring of customer lists. Development of AI, Machine Learning (ML), and Natural Language Processing (NLP) in AML compliance applications reduces the need for human reviews of false positives. This seemingly simple addition shortens the time required to get approvals.

Win-Win-Win

The best outcome for smaller companies is to move towards a tech-based compliance approach for their customers. This will help reduce the possibilities of fines for non-compliance and make it cost-effective for companies to carry out compliance.

KYC2020 provides affordable and effective AML compliance solutions that take advantage of latest technologies. KYC2020 is the best in class source for global Sanction, PEP, HIO, and Adverse Media data. We have designed our AI and NLP based screening and ongoing monitoring solutions to eliminate false positives and reduce manual work.

Contact Us for a free trial or to learn more about why KYC2020 is recognized as one of the world’s most innovative regulation technology solution providers by RegTech Analyst.

Use Cases Included:

• CIBC presenting a use case for AI/ML for streaming the onboarding process to near 89% that of human capabilities.
• KYC2020 use case on how using its DecisionIQ reduced the overall burden of reviewing false positives from 8 hours down to 30 minutes.
• AML professionals reported that AI/ML from AML had many applications in non-compliance functions including training, marketing, sales, and improved customer service.
• That all solutions are not a 100% replacement of human functions, rather, and an enhancement to extend beyond human insights.

There seemed to be a consensus among attendees that adaption of AI/ML tools will continue to move slowly. When asked about some of their reasons they said:

• A limited understanding of AI/ML application in the context of AML compliance programs.
• The idea that AI/ML is a “black-box” that isn’t easy to understand for executives, regulators, and compliance officers.
• The scrutiny of AML compliance programs by regulators to be able to demonstrate an understanding of what the AI/ML is doing to come up with the results.
• The actual cost savings and/or efficiency benefits brought on by using AI/ML.

To the last point, a recent report by PwC indicates that “While many favour the use of technology in combating fraud and/or economic crime, 37% of respondents believes that it produces too many alerts or false positives. This can lead to customer friction – a growing challenge for organizations as they seek to strike the right balance.” This balance is the key to delivering on the promise that AI and ML will reduce alerts and false positives.

Surmounting the False Positive Problem

While designing DesicionIQ, KYC2020 encountered similar challenges. We needed to determine what constituted the “right balance.” That meant we had to cast a wide net while KYC searching to insure we caught the maximum amount of hits. We needed to create AI/ML models that emulated the process a person would go through to clear the false positives. The difference being on a significantly larger scale than a human would be capable of reviewing. And in a matter of seconds.

But making a screening decision isn’t the only thing that matters here. Our CAMS(tm) certified AML specialists made sure that our system’s decision process are easily auditable in the same way you’d audit a human’s. To accomplish that, DecisionIQ provides all of its decision processes in a clear, readable “Proof of Work (POW)” format. The files are securely stored and easily accessible by compliance staff, auditors, or anyone else.

A Penny Poorer, a Pound Richer

Next-generation solutions that use AI/ML systems deliver overall cost savings and a more effective compliance regime. It can be challenging to convince management that that the initial investment is worth the spend. Yet, that thinking creates situations where a company is a penny richer and a pound poorer in the end. Advances in AI/ML will allow compliance professionals to make greater strides and reduce overall spending for management teams.

Over the years, we have seen the regulatory landscape of list screening expand from the usual sanctions lists to:

• Foreign (Politically Exposed Persons)
• Domestic PEPs
• Heads of International Organizations (HIOs)
• Relatives & Close Associates (RCAs)
• most recently adverse media

These lists and sources will only continue to expand. Consider the recent inclusion of digital currencies, such as Bitcoin or Ethereum into address into the OFAC as a searchable field. Pretty soon we will see other G38 members of the FATF do the same. There is no doubt this trend will continue to be a focal point for regulators to ensure regulated entities include more and more sources.

Regulators continue to push forward to the notion that more tools are needed to tackle these issues. A recent joint publication from the Fed Reserve, FDIC, FinCEN and the National Credit Union Association states that the joint agencies are committed to supporting “innovative efforts in combating money laundering, terrorist financing and other illicit financial activity.” This is a strong statement encouraging all those governed by the BSA that the use of AI and ML learning is an acceptable approach to “enhancing the effectiveness and efficiencies” of the financial sector the point where Fincen is willing to consider “exceptive relief under 31 CFR 1010.970 to facilitate the testing and potential use of new technologies and other innovations”.

Here in Canada, the Financial Stability Board (FSB) has also recognized in a 2017 report that AI and ML is playing a critical role in driving “to increase productivity and simultaneously reduce costs and risks, while complying with regulations”.

The Take Away

AI and ML capabilities have been steadily increasing over the last few years and to ignore them much longer would be at an organizations peril. It will not only affect its bottom line, but will most likely cost more due to the shear amount of data, alerts, and false positives that are increasing every day. It’s time to take a leap forward in AML compliance and start investigating the use of AI and ML to drive efficiencies and meaningful results.