Industry analysts, financial institutions, and technology experts all agree that artificial intelligence (AI) is going to meaningfully improve anti-money laundering (AML) compliance programs and outcomes. In the AML world, AI-powered technology can reduce false positives, automate investigation processes, and streamline reporting, so valuable investigator time isn’t wasted. AI also decreases risk by finding financial crimes that current transaction monitoring systems (TMS) are missing.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?
There are currently five distinct paths to leveraging AI in your AML compliance program:
Build Your Own
Some of the larger financial institutions have taken the homegrown path and hired an internal team of data scientists and analytics professionals. Like any build vs. buy decision, the downside to this approach is the difficulty of securing a top team of data science experts to build the first version of the system and then managing the on-going maintenance and improvements that will be required. Institutions dedicated to developing their own custom solutions can utilize machine learning models and algorithms from third-party sources like the QuantaVerse offerings available on Amazon SageMaker.
Hire a Consultant
There are large IT consulting firms familiar with the financial services industry that will attempt to build an AI-enabled AML system for you. With varying expertise in AML compliance and little insight into your financial crime mitigation processes, this requires a good deal of time and focus from your internal team and a big budget for both the consultant and the AI and data analytics subcontractors they generally bring into the job. This approach also suffers over time. To be kept current, data scientists must be hired internally, or consultants must be regularly re-engaged.
Work with an AI Services Firm
Specialty firms know a great deal about AI technology, but often work in many vertical industries so they lack specialized knowledge of the financial crime space. AI specialty firms often have a small “set” of algorithms that they “tune” for your application through trial and error testing.
Combine Multiple AI Point Solutions
Many vendors offer AI-powered point solutions dedicated to addressing very specific individual components of AML challenges such as entity resolution, link analysis, adverse media or sanctions screening. Financial institutions are wary of this approach due to its siloed nature. Implementing two or three independent solutions still leaves large productivity gaps in processes while unforeseen conflict between point solutions can create entirely new and unpredictable issues. This approach also suffers from the same challenges associated with “building your own” solution.
Implement a Packaged Solution
Packaged AI solutions are purpose-built for AML by integrated teams of data scientist and financial crime experts. These systems have been trained using scores of financial transactions and entity data from some of the world’s largest banks. They have also been tested in real-world environments to ensure they deliver outcomes that AML teams and their regulators have found meaningful. Today, vendors such as QuantaVerse offer packaged AI solutions designed to comprehensively address:
- Pre-TMS or False Positive Reduction
- Investigation Automation
- False Negative Identification
- Third Party Risk Management
Why Packaged AI Solutions
Successful digital transformation requires that AI and machine learning solutions are intrinsically assimilated into AML processes. As explained by ARC Advisory Group analyst, Craig Resnick, “AI becomes useful only when combined with deep domain expertise of the application and problem. However, given the tools and technologies available for AI implementation, most of the AI work can only be performed by data scientists. Some have seen increased success by creating focused teams made up of data scientists and SMEs, but this is neither practical nor sustainable.”
Resnick explains that, “the key change that is making AI pervasive and useful is the ‘verticalization’ of AI for specific businesses and processes. One way to achieve verticalization is by creating specific packaged applications.”
Benefits of Packaged AI for AML Solutions
There are many benefits of deploying and using packaged AI for AML solutions.
- Financial Crime expertise is built into the AI program from the ground up by firms that assemble a team of proven AI experts alongside experienced FinCrime professionals.
- Packaged solutions, such as those offered by QuantaVerse, are easy to implement and can be fully operational within weeks.
- Packaged solutions continue to be improved and updated by the solution provider based on continuous input from their customers and the regulators who work with those customers.
- AI for AML solutions can learn new financial crime patterns uncovered at one bank and, through “shared intelligence,” immediately identify the same patterns at other financial institutions.
- Designed to work with financial institutions’ existing TMS and standard industry processes, QuantaVerse solutions are already proven to successfully be deployed in FinCrime compliance environments.
- Offered by a single technology vendor, packaged solutions have straightforward licensing/pricing structures so there are no cost overruns or unpredictable surprises.
- Because AI for AML has been accepted and embraced in the industry and by regulators, the explainable technology is a safe investment. In fact, in December 2018, the U.S. Treasury Department’s anti-money laundering unit and federal banking regulators issued a joint statement that encouraged banks to consider “innovative approaches,” including new technology such as artificial intelligence, to enhance their AML compliance programs.
To learn more about QuantaVerse’s AI for AML solutions, please visit: https://quantaverse.net/try-ai.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.read more
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