Traditional technology used in anti-money laundering (AML) operations is proving inadequate in responding to modern-day challenges. Financial institutions (FIs) are exploring new technologies such as artificial intelligence and machine learning to improve efficiency and effectiveness of AML programs.
Data forms the foundation in AML and will be even more critical for applying AI and ML techniques. But data has been underutilized because FIs struggle with common data management challenges that are exacerbated by growing volume and speed of transactions. Regulatory scrutiny on model risk management is forcing them to rethink their approach to data management. Many are taking this opportunity to usher in changes that break down data silos and simplify data architecture.
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