Financial institutions are pouring more and more resources into transaction monitoring, yet alert volumes, disposition effort, and regulatory challenges rise.

By David Scott, EY Asia-Pacific

As regulators focus on this key area of anti-money laundering (AML) defense, financial institutions are looking to harness new techniques to satisfy regulatory obligations while avoiding spiraling costs, both operations and regulatory.

In the last six months, transaction monitoring (TM) fines against the region’s financial institutions have climbed into the hundreds of millions of dollars. To better meet regulators’ expectations, institutions need to:

1. Improve Quality in Existing TM Systems

Institutions require:

  • Well-Configured TM System – To better comply with regulatory expectations and to increase alert-to-case ratios, the TM system and processes need to be adequately designed and calibrated to address risks that the FI is exposed to on the basis of the size and complexity of its customer base, and transaction volumes
  • Quality Data – Monitoring systems are only as good as the data fed into them.
  • Regular Testing – With customer bases constantly changing, regulators expect management to give TM testing a top priority. It’s vital to use below and above the line, testing to regularly check parameters to ensure the TM system remains effective in its ability to identify unusual behaviors.
  • Well-Trained Staff – No matter how well-calibrated a TM system, it can always be undermined by its operators. TM staff need guidance on identifying risk patterns in client profiles and regular training on new AML risks.

2. Harness Technology to Improve Both Efficiency and Accuracy

Faced with growing transaction volumes, many financial institutions are looking at:

Robotic Process Automation (RPA) – In manual TM systems, human investigators often spend 70 percent of their time gathering data. RPA reduces TM resourcing constraints by assisting investigators to procure information more quickly and consistently.

Analytics – Analytics will never replace human investigators in the TM system. But it does offer institutions a complementary process to augment existing TM controls by sifting through historical customer data to predict potential suspicious activity. Already, analytics is helping financial institutions to identify activities that flag human trafficking and tax evasion.

Machine Learning – The next step will be to run machine learning over RPA, enabling bots to learn to triage alerts, prioritizing alerts investigators should look at immediately for decisioning and identifying those for likely quick closure.

It’s time to get your TM system quality tested and explore how technology can help you to avoid regulatory actions and reputational risk.