In a recent op-ed, President of NASDAQ, Adena Friedman argues for the potential of artificial intelligence (AI) in the financial industry, particularly in combating financial crime. While concerns about the risks associated with AI have been raised, the author emphasizes the importance of recognizing that not all AI is the same. AI has already been deployed in various financial market operations, and its capacity to detect and prevent financial crime is considered one of its most compelling use cases.
Financial crime is a significant global industry, and current estimates indicate that only a small fraction of illicit funds circulating in the financial system is intercepted by law enforcement. One reason for this is the restrictive impact of regulations that limit the use of data and advanced technology by banks. Criminals exploit the interconnectedness of the financial system and leverage new payment systems to evade detection. Therefore, high-quality data sets and advanced analytics technologies are crucial in effectively combating financial crime.
The author highlights the need for responsible data sharing among financial institutions across regions to enhance the identification of criminal activity. Regulators should also allow the industry to leverage the latest capabilities in cloud computing, AI, and machine learning to better respond to new threats and improve efficiency. Additionally, collaboration between the private sector, government, and law enforcement is essential, with the deployment of communication feedback loops to refine algorithms based on real-world outcomes.
In conclusion, the author urges regulators to reduce complexity and embrace the next wave of innovation to strengthen the integrity of the financial system through technology.
What needs to be done?
Embrace AI: AML providers should adopt AI technologies to enhance their detection and prevention capabilities, leveraging advanced analytics and machine learning algorithms.
Enhance data collection and sharing: AML providers should focus on building comprehensive data sets by collaborating with financial institutions, sharing anonymized transaction data, and utilizing consortium data approaches.
Strengthen collaboration: AML providers should foster closer collaboration between the private sector, government agencies, and law enforcement entities, facilitating information sharing, joint investigations, and coordinated efforts to combat financial crime.
Invest in explainability: AML providers should ensure their AI models and algorithms can provide end-to-end explainability to meet regulatory requirements and gain trust from regulators and financial institutions.
Stay updated with emerging technologies: AML providers should continuously monitor and adopt the latest advancements in cloud computing, AI, and machine learning to stay ahead of evolving financial crime threats.
We at Fourthline were thrilled to read Adena Friedman’s article on embracing technology in the fight against financial crime. We agree with most of her points, but we do respectfully disagree with her stance on enabling financial institutions to share data both from within and outside their own networks would greatly enhance our ability to identify criminal activity, this is not allowed in Europe, nor do we believe it is necessary.
Overall, AML providers should leverage AI technology, advocate for regulatory changes, foster collaboration, and prioritize explainability to effectively respond to the challenges and opportunities highlighted in the article.
By embracing such technology, regulated financial institutions can strengthen their AML efforts and contribute to a more effective and efficient financial crime detection and prevention ecosystem.
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