Like nearly all other aspects of business, anti-financial crime and compliance functions have been impacted by the disruption and uncertainty created by COVID-19. With financial institutions trying to squeeze more value from every dollar, financial crime functions are under pressure to drive efficiency and effectiveness across their programs. Yet at the same, the challenges of fighting money laundering and other financial crimes have not eased.
However, there are a few ways financial institutions can fight financial crime effectively amid today’s economic realities and the aftermath of the pandemic.
Maintaining Effective Financial Crime Detection in this New Normal
The COVID-19 pandemic has led both consumers and businesses to cut spending, resulting in fewer financial transactions than usual. In June, Visa confirmed that the volume of U.S. credit card transactions remained down from a year earlier in May. For business-to-business spending, Tradeshift is reporting transaction volumes are also down significantly year-over-year. Between these declines and local coronavirus restrictions likely impacting criminal operations, we can assume money laundering volumes are also likely down.
As a result, financial crime functions must ensure their detection thresholds are properly tuned to work throughout what will likely be an extended period of below average transaction volumes. For example, if an institution has rules and models built around set volume or dollar amount thresholds, it will need to reset these thresholds to still detect money laundering during this period of decreased activity.
This may mean lowering thresholds in proportion to the decline in transaction volumes. However, a more accurate approach would be to modify these rules to consider volatility (e.g. standard deviation) instead of static thresholds. Models built on volatility make it easier to evaluate behaviors in the context of average peer and individual activity. That means this approach continues to work even as averages change.
However, the persistent problem of data – how to use and manage it effectively – remains one of biggest inhibitors to rapid action. Financial institutions do not have the time to perform the extensive extract, transform, load (ETL) cycles required to make the necessary data available to the right locations at the right time.
That is why it is critical for a financial institution to have the right architecture and capabilities in place to properly use its detection data pipeline. From there, data science and data engineering teams are able to test, tune and re-deploy existing and new rules and models, supervised or unsupervised, against the same data pipeline.
Get Ready for New Money Laundering Schemes and to Re-Examine Onboarding Programs
Despite the global disruptions, criminals will still find new ways to launder money. To detect these new money laundering and fraud patterns, financial institutions will need to adjust or create new transaction volume thresholds. For example, we’ve already seen fraudulent scams surfacing around medical and food supplies, with proceeds then laundered as well as an increase in bribes to officials monitoring the movement of goods.
Consumer scams are also on the rise, with fraudsters filing false claims for programs like the Small Business Administration (SBA) lending program in the U.S. and the Coronavirus Business Interruption Loan Scheme (CBILS) in the U.K. In fact, the Federal Trade Commission estimates Americans have lost more than $77 million in fraud related to Covid-19 – and that’s likely just the tip of the iceberg.
While banks play a critical role in helping distribute the government lending program money, they must balance the need to quickly onboard new business clients, many of which urgently need money, with their know your customer (KYC) obligations. Given that it typically takes a bank an average of 26-27 days to onboard a new business customer, meeting KYC regulations quickly has proved challenging.
Some U.S. banks have been accused of prioritizing existing customers, and a few banks asked the Financial Crimes Enforcement Network (FinCEN) to let them collect customer and verify customer information after the loan application is processed, but this request was denied. On top of these challenges, financial institutions must continue to make sure their internal watchlists are updated according to any changes to sanctions that are implemented in response to COVID-19.
However, by streamlining KYC programs in a few crucial ways, financial institutions can find the right balance between faster onboarding and thorough due diligence. First, leverage third-party data providers and entity resolution capabilities to gather and process the information needed to meet KYC requirements faster. Using a mix of data sources – both internal and external, structured and unstructured – helps with onboarding both individuals and businesses by creating the most accurate risk profiles possible.
Next, ensure KYC is deeply integrated with your compliance backend and case management capabilities. This includes using connectors when integrating the third-party data just mentioned with your case manager. Such thorough integrations ensure analysts and investigators have all the required information needed in one place, making it easier to write reports and close out onboarding processes.
Improvements for the Long Term
By ensuring financial crime detection capabilities are more adaptable and flexible and by improving customer onboarding, financial institutions will be better prepared for the fight against financial crime in the months ahead. However, fluctuating business conditions and criminal activity are expected to become the new normal for the foreseeable future, and by taking action now, financial institutions will gain the agility and resilience needed to outlast the uncertainties of today.
