AI/ML’s Impact on Bad-Actor Detection: How it’s Redefining The Client Onboarding Risk Assessment Process
As the head of compliance at a fund administrator, ensuring that the funds they service are protected from bad actors is a top priority. This is especially critical when onboarding new investors for a new client’s fund, as there is a limited window to identify and mitigate any risks before the fund launch – typically something that the fund manager client wants to be done asap. One wrong move in identifying and mitigating risks during the onboarding of new investors for a new client’s fund could lead to substantial regulatory sanctions and penalties, loss of the fund client’s trust, and severe legal and financial consequences. It is a matter of life and death for the fund administrator.
Traditionally, risk assessments when onboarding new investors has been a manual process, relying on human judgment and limited data sources and plenty of Excel. However, with the advancements in artificial intelligence and machine learning, the way we assess risks is being redefined. This application of this new technology will not only radically change the time it takes and labour costs incurred running risk assessment the ‘old way’, but will also vastly improve the risk to the fund administrator or taking on the wrong investors into the new fund.
AI and ML have a wide range of capabilities that make investor risk assessments well-suited for bad-actor detection during the onboarding process. The technology can improve data analysis and pattern recognition, automate manual processes, use machine learning algorithms to adapt and improve over time, integrate a wide range of data sources and real-time monitoring of clients to detect any changes in the risk profile.
Here are 5 ways AI/ML is changing the game in assessing the risks of onboarding a bad-actor client
- Improved data analysis and pattern recognition: By analyzing large amounts of data, AI can identify patterns and connections that may indicate a potential bad actor. This allows for a more accurate risk assessment than traditional methods.
- Automation of manual processes: By automating manual processes, AI can increase efficiency and reduce the risk of human error. This allows for a more thorough risk assessment in a shorter amount of time.
- Use of machine learning algorithms: Machine learning algorithms can be used to continuously adapt and improve the risk assessment process over time. This allows for a more dynamic and adaptive approach to identifying bad actors.
- Integration of a wide range of data sources: AI can integrate a wide range of data sources, including social media and other online platforms, for a more comprehensive risk assessment. This allows for a more holistic view of a potential bad actor.
- Real-time monitoring of clients: AI can be used to monitor clients in real-time, detecting any changes in their risk profile. This allows for a more proactive approach to identifying and mitigating risks.
In conclusion, AI and ML are revolutionizing the way we assess the risks of onboarding a bad-actor client. By using these technologies, we can improve the accuracy and efficiency of the risk assessment process, ultimately protecting our clients’ funds from potential bad actors. As the head of compliance, I am excited to see the future developments in AI/ML and how they will continue to shape our approach to risk assessment.
Mesh ID Risk Assessment: Please contact us at firstname.lastname@example.org to learn more about this powerful toolkit and how it can help you speedup, derisk and modernize your investor risk assessment process.