Results from a recent survey by McKinsey and the International Association of Credit Portfolio Managers (IACPM) shows that firms have made steady progress in using new data and techniques for credit portfolio management, but also demonstrates that challenges remain around technology, data, talent, and integration of new use cases like climate and environmental, social, and governance (ESG) risk.
Respondents are obtaining data from sources like automated client financials; internal credit behavior data and cross-product data from internal sources; and credit bureau, economic forecasts, and news data from external providers. Alternative data adoption has grown with more than half of respondents currently using, piloting, or considering news media, social media, or third-party account data. Machine-learning models are gaining traction with financial companies in risk scoring of small and medium-size enterprises and in early-warning systems but adoption levels still remain low. With SME portfolios specifically, about 30 percent of respondents reported that they have automated more than 30 percent of their decisions. In addition, in the SME space, respondents report a significant benefit in turnaround time, with 37 percent of participants reporting a more than 10 percent decrease.
The deployment of machine learning and other analytical techniques has brought several challenges to the forefront. Survey respondents noted various barriers to increased adoption of innovative data solutions and advanced analytical methods including data quality assessment, talent availability, and difficulty in validating and explaining new techniques.
Of surveyed financial companies, 86 percent cite climate, environment, social and governance issues as the biggest challenge in credit assessment.
ESG risks are composed of environmental risks arising from operations and consumption of the services and products of the organization; social risks arising from how the organization treats people, including employees, customers, and the communities in which it operates; and governance risks arising from poor practices in the organization’s interactions with its shareholders, board, and management. These risk factors may have a positive or negative impact on the financial performance or solvency of an entity, sovereign, or individual.
Climate risk falls within the category of environmental risk, and is connected to both the direct and indirect effects of physical hazards associated with climate change. Direct damages are caused by, for example, hazards like floods, wildfires, and hurricanes; indirect damages are consequences such as potential increases in insurance premiums and the effect on at-risk communities’ living standards. It is also connected to transition risk: the policy, technology, and regulatory risk inherent in transitioning away from an economy overly reliant on activities that produce greenhouse gases.
Many financial institutions are now assessing their portfolio’s exposure to climate risk, either due to regulatory requirement, or to test the hypotheses that hurricanes, floods, blizzards, tornadoes, wildfires, and other natural hazards can inflict billions in damages across loan portfolios in any given year. Banks that have started climate stress testing are now considering whether to build new credit models or to tailor existing ones for stress testing. Respondents were evenly split into thirds, saying they are developing new loss models, using current models, or are not yet exploring. In addition, analyses on loss scenarios due to climate stress were more concentrated on midmarket, corporate, and CRE portfolios (more than 50 percent of banks for each), with fewer banks (less than 40 percent) conducting these analyses on SME portfolios.
The survey indicates that portfolio managers have only recently started to consider how climate and ESG risks affect risk identification and risk measurement, including obligor credit ratings.
They now need new data, tools and processes to analyze climate stress loss and climate scenarios; they must also evaluate how climate risk assessment can be integrated with existing credit processes.
As many existing risk-assessment tools were not built for the requirements of climate assessment, financial institutions will need an open architecture that can support new methodologies for data quality, standardization, and collection.
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