While dual risk rating (DRR) has ties to Basel II, its impact on an organization can go far beyond regulatory compliance. A sound, efficient DRR process can strengthen underwriting and enhance risk management. It can also increase efficiency and, by enabling more accurate calculation of all reserves, potentially free up money for revenue-generating investments.
Among small and middle-market banks, DRR adoption has been slow to date due to the cost and disruptive nature of implementation; however, that trend is poised to shift. Regulatory developments such as the Current Expected Credit Losses (CECL) standard are motivating banks to consider dual risk rating. Further, banks increasingly have access to the data necessary to perform dual risk rating, and the technology that enables it is now available as part of many major banking systems or suites—making DRR more feasible than it has been in the past.
Dual risk rating (DRR) is a methodology for analyzing credit risk born from Basel II, published in June 2004 to create standards for governing capital adequacy. Under DRR, a bank calculates certain metrics, including a borrower’s probability of default (PD) and a loan’s loss given default (LGD), to determine the overall expected loss (EL) presented by a relationship— thus, creating a sharper lens for assessing its credit portfolio.
If implemented properly, DRR offers risk management, regulatory compliance, and efficiency benefits—and potentially more.
Regulators continue to emphasize the importance of a robust data-gathering and management strategy throughout the enterprise, with particular emphasis on the credit portfolio. Regulatory measures increasingly mandate banks to assess portfolio risk with added granularity and objectivity. For example, FASB’s CECL standard specifically requires DRR elements—PD, LGD, and EL—over the life of a loan. While there are key differences that distinguish CECL and DRR, the implementation of DRR will help a bank be better prepared to adjust and react to future regulatory measures.
Because dual risk rating increasingly relies on model-based quantitative analysis rather than “expert” judgement, it brings greater objectivity to the risk rating process. The method’s data-driven and statistical foundation also improves process accuracy and consistency.
Additionally, the objectivity of the DRR process offers banks confidence in employing data-driven decision-making capabilities for loan approvals (auto decisioning) and pricing (risk-based pricing), which can improve process efficiency significantly. Efficiency, in turn, can improve the customer experience, helping a bank drive toward greater competitive advantage.
Finally, greater granularity and precision increases accuracy in calculating capital reserves, potentially allowing a bank to take a less conservative approach to reserves and free up money for revenue-generating investments.
Dual risk rating has traditionally been time consuming and expensive to implement. Implementation can also be disruptive to existing processes and taxing to a bank’s human capital, particularly its underwriters and risk modeling function. It likely also requires investment in new technology, including financial spreading tools, a digital platform for risk rating, and technology integration. Despite the significant potential benefits, we have seen few mid-market banks voluntarily adopt DRR to this point.
Yet, we expect adoption to increase, for two reasons:
Banks of all sizes, not just large institutions, but specialty, niche, and regional banks—have access to more and more data for predicting risk, and the technology used to gather, store, and analyze client and loan data is more accessible and less costly than ever. Additionally, leading financial services technology companies have integrated DRR frameworks into their bank technology solutions and suites, making it easier to purchase the DRR capabilities “off the shelf.” Further, as more banks adopt DRR, the roadmap is becoming clearer thereby lowering the learning curve and implementation costs.
When implemented properly, the accuracy and granularity achieved through DRR can transform the way a bank thinks about and reacts to risk. This can have far-reaching benefits, as growth aspirations and increasing pressures to create competitive advantage will continue to drive strategies that enable banks to differentiate themselves in the loan process. As that happens, DRR will continue to take hold due to the advantages it offers and the greater accessibility of the data and technology that enable it.
If your bank is thinking about adopting a DRR process, the following are some high-level planning questions and considerations:
How will projected growth affect risk management from the perspectives of policy, process, and technology?
Does your bank have current risk rating limitations, such as lack of objectivity, efficiency, or granularity?
What are the technology and data implications to your organization? Do you have the foundational infrastructure to support DRR?
What is the potential impact to revenue-driving lines of business?
Do specialty lines of business and products have specific needs that must be accommodated?
How will you need to change policies and procedures?
Our team brings extensive experience to this discussion to help you avoid typical pitfalls and accelerate the benefits of a DRR implementation. We have helped various banks evaluate, develop, and implement key elements of a comprehensive DRR approach, including process design; potential for leveraging banking capabilities such as auto-decisioning and risk-based pricing; automation opportunities to drive efficiency; risk rating methodology, including grades, scales, definitions, and scorecards; and required technical infrastructure.