The online shift is harming security compliance
As business has shifted online amid the COVID-19 pandemic, spotting financial crime is more critical and challenging than ever, and it is clear financial organizations are struggling to meet their regulatory obligations.
Anti-money laundering (AML) regulations require financial institutions issuing credit or allowing customers to open accounts to complete due-diligence procedures to detect money laundering.
Financial institutions are responsible for verifying customers, knowing where large sums of money originated, monitoring suspicious activities and reporting these transactions to the appropriate authorities. But how can organizations detect crime and streamline compliance as business has shifted online?
Money laundered globally ranges from $800 billion to $2 trillion each year. Financial regulators levied larger fines for AML violations in the first half of 2020 than they did for all of 2019, as institutions repeated past compliance mistakes, causing reputational damage.
While identity proofing helps verify customers, it only addresses part of the problem (screening out potential money launderers from newly created accounts). Financial institutions and other regulated entities must also integrate transaction monitoring solutions to detect and report suspicious financial activity of existing customers that may be facilitating money laundering.
Leveraging multiple solutions to combat financial crime and meet compliance mandates may create loopholes and gaps in an organization’s security net, which can ultimately fail to spot money laundering efforts. Enterprises can best spot suspicious activity by implementing a single platform that analyzes financial data and searches for anomalous behavior and activities that suggests money laundering; simplifies case management by documenting suspicious activity and submitting regulatory filings and provides a comprehensive and holistic view of customer risk, including upfront KYC processes and ongoing AML screening and transaction monitoring.
Legacy systems, which were not built to support distributed teams, still rely on static rules that don’t respond to new patterns and simply can’t keep up. As a result, compliance teams are unable to effectively work remotely and waste a lot of time on false positives, sometimes missing suspicious activity. Machine learning algorithms can learn from the results of every investigation which, in turn, leads to better predictability, detection of suspicious activity, and reduced compliance burdens.
Financial crime and bad actors have taken advantage of the work-from-home world and bank’s digital channels, which makes it easier to disguise money laundering and suspicious activity. To stay ahead of this rapidly evolving fraud curve, financial institutions should implement a comprehensive solution that includes upfront identity proofing to ongoing transaction monitoring and case management to lower their risk profile by flagging suspicious activity and preventing bad actors from creating accounts in the first place.