AML Software in U.S. Financial Industry
Identify how anti-money laundering (AML) software is used in the U.S. banking/financial industry, including how/why banks use it, what the software protects banks from and how, who exactly uses it, metrics of success for this FinTech, and if pre-compiled, the market size of this software in the U.S. banking industry.
Who Uses AML FinTech
- Anti-money laundering (AML) efforts are required to be used by financial institutions in the U.S. through the Bank Secrecy Act. This includes banks, trust companies, credit unions, broker-dealers in securities, mutual funds, Futures Commission Merchants, Introducing Brokers in Commodities, and Money Services Businesses.
- Additionally, foreign exchange dealers, check cashers, money transmitters, issuers/sellers of checks/money orders, and issuers/sellers of prepaid access (i.e. insurance companies, casinos, mortgage lenders, dealers of precious metals/stones, and housing government-sponsored entities) are all required to employ AML efforts.
How Financial Institutions use AML Technology
- Approximately 53% of financial institutions in the U.S. use on-promise AML transaction monitoring systems, while 47% use off-premise versions.
- Additionally, 65% of financial institutions use ASP off-premise AML transaction monitoring systems, while 35% use cloud systems for off-premise monitoring.
How AML FinTech Protects Financial Institutions
- Large-bodied banks will dedicate hundreds or thousands of employees to operating AML technology and ensuring its compliance to regulations, while smaller banks will typically dedicate between 20-30 employees to this task.
- AML FinTech uses machine learning and data analysis to calculate the risk of real-time customer transactions, even during the onboarding process.
- According to data commissioned by Guardian Analytics and published by Celent in May 2019, the use of AML FinTech can reduce false positives of money laundering and increase efficiency by 25%-40%.
- Next-generation AML technology is expected to be able to reduce false positives by 30%, increase low-touch closing of alerts by 40%, and improve investigation efficiency by 25%.
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