With the heightened burden and increased costs placed on correspondent banks today to ensure compliance across their portfolio, many major institutions have instead resorted to “de-risking” certain segments, markets, and jurisdictions. De-risking largely came about as an unintended consequence of regulations that left banks with limited alternative risk reduction methods, at least at the time.
Ironically, de-risking has not eliminated the risk of illicit flows, but simply reallocated these risks to far less transparent channels, be they overburdened local banks, or the informal market.
This movement of traffic into opaque informal channels has undermined the integrity of the broader financial market, as it exposes the global financial system to unsupervised, non-transparent, high-risk flows (World Bank, 2016). Without adequate monitoring of this traffic, criminals can transact with little risk of being detected.
Banks must cope with this new reality, as it is clear that de-risking is not the solution. Fortunately, extensive growth in technologies such as machine learning, presents the opportunity to both increase efficiency and decrease the costs of financial crime risk management. Doing so would allow for a reversal of current de-risking trends, as costs would be reduced, efficiency would be increased and transparency would be enhanced.
De-risking is largely a response to the costs of due diligence and risk mitigation exceeding the profits to be gained from a given respondent/correspondent relationship. In such cases, the bank or region is de-risked for fundamentally commercial reasons (Oxfam, 2015). In other instances, banks have undertaken mass de-risking in reaction to increased regulatory scrutiny (Financial Conduct Authority, 2016).
According to Oxfam and the British Bankers’ Association, banks have terminated nearly half of all previously existing correspondent relationships since 2002. As of 2015, upwards of one-third of all correspondent banking relationships have been dropped, and the trend does not seem to be changing (Oxfam, 2015/World Bank, 2018).
Even when a country is de-risked, the monetary flows to and from the region continue. The true change that has occurred is an end to the oversight of these flows; the money still circulates across borders.
As relationships are terminated, the burden is placed on smaller correspondent banks and remittance agents who lack the capacity to manage these client volumes and enact adequate financial crime controls (Oxfam, 2015). In such cases, the increased operational pressure actually increases financial crime risk.
Worse yet, customers not taken on by these local banks nevertheless continue to transact through even more opaque and unregulated channels (World Bank, 2016). The assets in these informal channels may intermingle with illegitimate or illicit funds garnered from criminal activity, or raised for terrorist financing purposes (ACAMS, 2018). As more customers transition into the informal market, these markets themselves grow and provide the infrastructure necessary for criminal organisations to transact with ease (Haley, 2018).
It is clear that banks must rethink their current approach to financial crime risk management. De-risking does not reduce risk. Going forward, banks must adopt new, cost-effective, and efficacious methods of carrying out financial crime risk management across their correspondent relationship portfolios.
In an effort to quickly enact controls, correspondent banks have put in place unsustainable systems and processes, driving up costs with little evidence of increased overall effectiveness. In conjunction with limited access to respondent data, the current compliance technology infrastructure has left the industry seeking better tools for managing their financial crime risk (Financial Stability Board, 2018).
While most correspondent banks conduct many of their procedures manually, often compensating for ineffective systems, it is clear that with the rise of machine learning and big data technologies, untapped efficiency gains remain (Financial Stability Board, 2019).
By utilising these new technologies in conjunction with a risk-based approach, banks are now able to leverage a universe of data to assess the probabilistic risk exposure of any given respondent bank on an ongoing basis. Today, the true answer to the rising costs of compliance resides in the untapped technological potential of this data-rich industry.
If banks are to begin effectively managing their correspondent banking risk exposure, they must have the right tools in hand.
At its core, de-risking is a simplistic response to a complex problem, based on a general lack of transparency, limiting banks’ ability to understand financial crime risk and how it should be mitigated.
This is where Elucidate can help. Utilising a hybrid of expert-driven and machine-learning modelling, the Elucidate FinCrime Index (“EFI”) is able to transparently assess the risks a bank and/or its counterparties are facing. It enables both correspondents and respondents alike to swiftly and effectively articulate risk exposures through a holistic assessment of data and control outcomes*. By creating a platform enabling far greater transparency between counterparties in the correspondent banking space, the EFI enables the reversal of de-risking practices in exchange for effective risk management practices across the global financial network.
*With data privacy being a pre-eminent concern for both customers and banks alike, the EFI has baked in privacy controls, enabling an unprecedented level of transparency between banks without the need for extensive data disclosures between counterparties.