Recent advances in technology and an increasing internal focus on efficiency and effectiveness present an opportunity for compliance professionals to yield significant organizational benefits. However, how can these advances be used in real-world situations, and how do they meet expectations of auditors and regulators?
In this webinar, Bci Miami, the largest Chilean bank operating in the U.S. and QuantaVerse, explore the role of technologies, such as AI and machine learning, in automating complex and human-led processes in AML programs. The webinar covered the following topics:
- The many possible opportunities available for automation (where AI makes sense, and where it doesn’t)
- How to apply recent innovations to your enterprise (e.g., what are machine learning and natural language processing and how can these techniques help your compliance department)
- Case studies of successful automation including what worked, what didn’t, and what the impact was
- How auditors and regulators view the advances in technology and how you can validate the results
Report: How Financial Firms Are Using Artificial Intelligence and Machine Learning to Meet AML Demands of Today and Tomorrow
It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.
Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...
While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.