A top concern for financial institutions, especially those that offer correspondent banking services, is third-party, counterparty risk, sometimes referred to as pseudo-client risk. This type of risk is specific to the behavior of a financial institution’s client as well as the behavior of their client’s clients and counterparties. Within the corresponding banking space, these counterparties could be considered third-party clients. This presents an enormous amount of risk to financial institutions despite these counterparties not being their own clients.
Ensuring that financial institution clients conduct KYC on their clients is a difficult proposition. Site visits and questionnaires rarely offer comprehensive responses and at best evaluate the process – not the outcome. This is further compounded by the uneven, and often shifting, regulatory obligations internationally. Regardless if institutions take on risky clients, or clients that aren’t risky but transact with risky entities, traditional rules-based systems provide very little ability to a.) see risk, b.) assess risk, and c.) make a decision whether or not to accept the risk.
Leveraging a third-party client risk management solution can help go that last mile of due diligence by enabling an understanding of who that third-party client is as well as who their counterparties are and bring those transactions to an institution’s risk management team for further investigation to determine if there is an economic purpose apparent in those transactions. Such a solution can then determine whether or not the transactions are consistent with expected behavior and even predict day-by-day behavior of account utilizing historical data.
The QuantaVerse Third-Party Risk Management solution provides risk managers with better insights into the variances or anomalies in account activity that might indicate risks of financial crimes, or that suggest an account is being used for something other than its stated purpose. This type of analysis is a growing regulatory burden driven by the need to understand the risk profile of clients as well as clients’ clients.
The solution measures values, volumes and velocity of accounts, predicts future expected activity, highlights risk indicating variances, and confirms whether the account is being used for the stated purpose. This analysis is conducted on an ongoing or periodic basis, which is adjusted according to the financial institution’s management and reporting requirements.
In terms of transaction types, the solution supports: in/out bound, SWIFT messages, Fed/Chips, and all jurisdictions e.g. such as CHAPs and SEPA payments and will identify and alert risk indicating anomalies for both clients and pseudo-clients and enables users to drill down into the correspondent bank and contributing pseudo client activities that are creating these risky anomalies.
Once anomalous account activity is identified, financial institutions can then utilize their own investigative resources to dig deeper into the issue or it can leverage the QuantaVerse Alert Investigator to intuitively drill down and view information regarding the transacting parties, the transactions, network visualizations, risk scores, etc. The Alert Investigator frees investigative resources to focus their time, skills, and talents on the most complex financial crime risks and investigations.
The traditional process of collecting KYC data on counterparties is difficult and labor-intensive. To mitigate third-party AML risk, QuantaVerse has developed new solutions which assist financial institutions in better assessing third-party risk by providing them with a holistic view of counterparty account activity versus expected behaviors.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?