Banking standards for monitoring and controlling large exposures have uncovered a need for tools that can streamline credit risk management systems and create a single enterprise-wide view of risk as an early warning system against future crises
Managing concentration risk is certainly not a new activity for banks. However, the codification of regulations designed to limit large exposures is now pushing banks of all sizes to look for new and more efficient ways to manage and mitigate this type of risk. Although these new rules apply to larger players only, developing an understanding of concentrated exposures should be a foundational element of any effective risk management strategy – something that all organisations must strive to do.
By January 2019, banks with $50 billion or more in consolidated assets will be required to limit large exposures to 25% of Tier 1 capital (15% for global systemically important banks), under standards published by the Basel Committee on Banking Supervision in 2014. Regional regulators have used this template to create their own versions of these rules, including the proposed Federal Reserve Board’s single counterparty credit limits in the US, Europe’s Common Reporting Large Exposure Guidelines and MAS 369 in Singapore. The main requirements are largely the same; however, banks must limit exposures to large or connected counterparties and monitor these relationships on an ongoing basis to mitigate the effect of any financial problems relating to one or more of these organisations.
Although these regulations will be enforced only for larger organisations, banks of all sizes should take this opportunity to reassess their internal systems and processes used to manage credit risk. For many financial firms, however, this means facing up to a long-running reliance on manual data management tools such as spreadsheets, as well as a complex warren of legacy systems and processes. Finding out how best to untangle and streamline current infrastructure to create a single view of risk on an enterprise-wide basis should be a top priority for the banking sector.
Improving data aggregation
The aim for any bank navigating the current regulatory landscape, regardless of size, should be to create effective ways of monitoring and managing risk that will satisfy regulators and enable the organisation to remain competitive. But, while regulators have been very clear about the need to manage counterparty credit risk, an important piece of the puzzle is missing for many banks – how to develop the right infrastructure to manage this risk.
Rachel Anderika, senior principal at Promontory Financial Group, a regulatory consulting firm owned by IBM, says data aggregation is the missing puzzle piece. “Banks must be able to aggregate data to understand their overall concentrations and figure out their exposure, as well as what’s happening in the market from a macroeconomic standpoint,” she explains.
Of course, banks have always monitored concentration risk, but in the decade since the last global financial crisis took hold it has become apparent this risk must be monitored on an enterprise-wide basis and proactively managed. “Banks need accurate, reliable data to ensure they become aware of any areas in which a concentration is introducing more risk than expected,” says Kathryn Dick, a managing director at Promontory. “A bank can then adjust its reserves and change its strategy while there is still time.” This requires the ability to map all counterparty data held across the organisation to a single record that will allow the bank to accurately identify and illustrate overall exposure.
Even without new regulations on the horizon, it is the ideal time to address this issue, according to Kathryn Dick. “The time to do this is not when the house is on fire,” she says. “This cannot be done in the middle of a credit downturn.”
Apart from the obvious point that it would already be too late to start addressing credit problems in the midst of a crisis, banks would have neither the time nor the money to implement major changes. By reassessing this part of the business now, when markets are relatively stable, it is possible to create an early warning system in advance of any future problems.
“Not every management team would be willing to invest money in a credit risk system when losses are down, times are good and problem portfolios are small,” adds Rachel Anderika. “But the best managers – and those that weathered the last financial crisis well – did just that.”
A new focus
To develop the kind of enterprise-wide credit risk management systems that will satisfy regulators and create a competitive advantage, banks need to rethink current approaches to data collection, monitoring and management. Too many organisations still rely on spreadsheets and manual analysis, through which human error can lead to major reporting inaccuracies. It is also often extremely laborious and time-intensive to create and maintain these manual systems. This activity ties up important human capital that could be more valuable to other parts of the business.
In addition, from a compliance perspective, regulators will expect timely reporting of this information. “Even if a bank does not need to know its credit exposure in real time, regulators will expect it to have the capacity to monitor its credit exposure on at least a daily basis,” Kathryn Dick says. “There are banks that either still cannot do this or are doing it based on a significant amount of manual intervention. This will more than likely result in inaccuracies and delays in terms of their reporting capabilities.”
Many of the banks that still rely on such tools also lack the means to connect various systems across the organisation in such a way that they can “talk” to each other, Kathryn Dick adds. This is often the case for banks that have grown via mergers and acquisitions activity. An organisation might work hard to create a cohesive brand and connect business lines to benefit from synergies following an acquisition but, behind the scenes, data management infrastructure often remains untouched – a disparate jumble of legacy systems. “When a bank passes the $75 billion mark, data complexity generally increases, as do regulatory expectations,” Rachel Anderika says. “So, all of a sudden, this bank might be faced with a completely different set of regulatory expectations. From a data perspective, the organisation needs to develop an interconnected system, rather than relying on an infrastructural hodge-podge with reporting derived from multiple sources.”
Taking a proactive approach
In addition to creating a single view of risk across the organisation, banks also need to take a proactive approach to credit risk management – not just viewing a historical report at regular intervals, but actively monitoring and managing this type of risk on an ongoing basis.
“It would be a mistake for a bank to approach this issue by bringing the relevant information into a reporting database and churning out a monthly report,” says Catherine Duggan, global offering manager in credit risk at IBM. “At the end of the month, that report might tell a bank its concentration risk exposure is fine, but it’s not an accurate reflection – it’s a historic view that will not enable the bank to effectively respond to market change.”
In order to both satisfy these new regulatory requirements and remain competitive, banks need automated tools that will enable ongoing credit risk monitoring and management. Such systems must balance the assessment of new opportunities in relation to existing concentrations, which is crucial to the kind of analysis that underpins the decision-making process for a bank. “In the new regulatory environment, banks must be able to evaluate risk as of time-zero,” Catherine Duggan continues. And, while risk managers require tools that can present a consolidated view of risk to support the analysis of the organisation’s current position, the ability to assess future exposure – and potential changes to that exposure – is also crucial.
“Risk managers need to be able to see their organisation’s position today, but also have a view of future risks, such as loans that are awaiting approval,” Catherine Duggan says. “This allows for greater analysis of the impact of future decisions on a bank’s risk profile and whether further investigation is necessary.” Such tools can also provide insight into the potential impact of dynamic credit structures that might need to be funded in a certain scenario, including accordion debt or standby letters of credit.
Finding the right solutions
There are tools that can create a single view of risk across an organisation and enable proactive management of that risk. “By using a solution such as IBM’s Algo Credit Manager, for example, this problem is quite easily solved,” says Catherine Duggan. IBM has worked with clients to turn spreadsheet-based systems into a fully functioning infrastructure that can generate a single view of risk on demand. It also uses automation to proactively manage risk, reducing manual data entry and saving valuable time.
“The solution works in the background, providing information as needed and also acting as an early warning monitoring tool,” Catherine Duggan adds. “It takes the heavy lifting away from having to monitor risk, and allows the bank to focus on forward-looking strategic decisions relative to market changes.”
The new rules around concentration risk have established concrete requirements for activities that many banks already undertake, but this has also provided a much-needed opportunity for banks to reassess the relevant systems and processes they use to do this. By taking this opportunity now, in a relatively stable period for financial markets, banks can create an enterprise-wide, early warning system for credit risk management in advance of the next market crisis. “These tools solve a variety of different problems, not just regulatory and supervisory requirements,” Rachel Anderika says. “Putting such a system in place now is not just good risk management, it is the standard for risk management.”
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