Anti-money laundering (AML) efforts are nothing new to the financial industry, but the regulations and technologies that are driving these efforts are. With new, complex regulations and technologies like artificial intelligence (AI) and machine learning (ML) changing the traditional AML approach, financial institutions must update their tactics to take on malicious threats.
To learn more about mastering the reinvention of AML, we spoke with Michael Schidlow, the head of financial crime and compliance and emerging risk training for HSBC’s global and internal audit function. Michael directs the financial crime risk training program and designs courses that help financial institutions fight financial crimes across the globe.
“The AML landscape is everchanging,” said Schidlow. AML and fraud were once siloed functions that are now linked due to an increased understanding of financial crimes and increases in regulations. “It’s a constant challenge to keep up with what criminals are doing, and how criminal activity can potentially interface with a financial institution controls,” explained Schidlow. For a financial institution to be successful, it must be reactive but also preventative.
Many financial institutions are looking to the vast amounts of data collected to uncover potential bad actors. “Leveraging data analytics helps pierce across data sets, across information silos and really unveils who the operatives or beneficiaries are,” said Schidlow. “We continue to adapt to financial crimes with training, functions, and tool systems.”
AI and ML are helping financial institutions analyze and extract data to aid with AML efforts. “Across the board, financial institutions are leveraging artificial intelligence both for customer engagement purposes but also anti-financial crime purposes,” said Schidlow. AI and ML are being used to expedited processes like name matching that leaves human employees open to focus on more intense tasks.
While AI and ML offer financial institutions and many other industries the opportunity for data analytics, the potential is often underestimated. “There is a lot of demystifying that needs to go into AI and ML and the difference and compatibility between those two things are data analytics,” explained Schidlow.
Learn more about the power of AI and ML to fight fraud and other financial crimes? Click here.
