SARs overload slowing efforts to combat financial crime
By 
Martin Woods2020-07-29T14:43:00
For the global AML community, there is a need to recognize too much valuable time is spent filing too many low-value suspicious activity reports that will never become the subject of any law enforcement action, writes Martin Woods.
THIS IS MEMBERS-ONLY CONTENT
You are not logged in and do not have access to members-only content.
If you are already a registered user or a member, SIGN IN now.
Related articles
-
ArticleAnalysis: Improving SARs effectiveness takes more than act of Congress
In the wake of 2020’s FinCEN Files leaks, the U.S. Treasury this year will undertake a thorough reevaluation of the country’s AML program under the Bank Secrecy Act.
-
ArticleCompliance should consider a well-balanced information diet
In comparing information to food, the outcome might be considered to be the same when too much is consumed, writes Martin Woods.
-
ArticleMore data, more problems with FinCEN international transfer proposal?
A recent international wire transfer rule change proposed by U.S. regulators could go a long way toward combatting terrorist financing, but the increased transaction reporting may overwhelm an already taxed system, writes Martin Woods.
More from Opinion
-
ArticleGSA’s draft AI Clause turns governance into a contractual mandate
A sweeping proposed federal procurement clause would push AI oversight out of policy decks and into compliance operations, vendor management, and real-time control testing.
-
OpinionFrom enforcer to coach: Engineering compliance that happens at speed
New Gartner research reveals why traditional risk management is failing to keep pace with modern risks, and outlines how compliance leaders must enable organizational risk owners to build an instinctive Risk Reflex.
-
OpinionLeveraging COSO to mitigate AI risk: A step-by-step guide
AI elevates compliance, or exposes it. The technology presents compliance leaders and lawyers with an extraordinary opportunity to elevate their roles, as well as an equally extraordinary risk of accountability when AI fails, misleads, discriminates, hallucinates, or generates unreliable outputs.