Did AML Optimisation arrive just in time?

BAE Systems tunes money laundering detection tools with analytics, machine learning

The rising costs and increased reporting requirements for banks and financial institutions to respond to anti-money-laundering (AML) regulations are spurring them to adopt new technology to identify and report suspicious activity to authorities.

Financial regulators require entities under their watch to validate AML programmes and prove the efficacy of models used to identify money laundering, which morphs to avoid detection.

Financial institutions require intelligent, data-driven tools to measure, enhance, and optimise detection logic for their AML programmes, thereby reducing cost by identifying truly suspicious activity while also reducing risk both to the institution and those accountable for compliance.

"The NetReveal Optimisation Module has tight integration with NetReveal Transaction Monitoring to tune alert parameters for optimal performance to reduce maintenance costs and exposure to AML risks and meet increasing regulatory reporting requirements."

451 Research

BAE Systems NetReveal® AML Optimisation Module works with the BAE Systems NetReveal AML Transaction Monitoring product to analyse outcomes and improve efficiency using statistical analyses and machine-learning techniques to recommend model configurations with the means to test, simulate and export AML models to production with an audit record.

Download the full report to discover 451 Research’s take on the BAE Systems NetReveal AML Optimisation module.