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  • Credit Risk
  • Early Warning System
July 26, 2018

Spotlight on credit risk as cycle shifts

Technology and analytics can plug gaps in risk monitoring and boost surveillance

Markets have been abuzz with talk of a turn in the credit cycle with rising rates and dipping liquidity. Traditionally, a turn in credit cycle has had predictable consequences: rating transitions, higher default rates and more investments in credit risk monitoring. To understand whether banks and asset managers are better placed to cope with a cyclical change, CRISIL GR&A’s SPARC (Shared Platform for Assessing Risk of Counterparties) team has looked at granular data with a microscope and can assert that this time will not be different. It could be worse.


While it is unclear as to when the credit cycle will turn, we find a material increase in leverage across industries, an increase in the proportion of lower-quality loans and stretched credit market valuations. Data also suggests that risks in the US are higher than in Europe and could emerge faster too.


In addition, the SPARC team has assessed industry risks and conducted a stress analysis of ~2,900 non-financial companies (representing $10 trillion of gross debt) listed in the US and Europe. The analysis of these firms across 17 industries indicates significant downside risks if the tide turns.

  • At the aggregate level, we estimate that the share of gross debt with high levels of riskiness (net leverage and interest coverage levels equal or worse than the BB rating bucket) could rise from 15% to 28-33% and 9% to 18-27% across the US and Europe, respectively, driven by a cyclical decline in earnings and widening of credit spreads.
  • The media and entertainment, housing and consumer cyclical sectors remain most vulnerable given higher earnings downside risk, debt maturity headwinds and ongoing industry-level shifts. Other sectors directly linked to telecom and resources are likely to face higher liquidity and leverage risks during a downturn.

While latent risks have risen, the monitoring mechanism is a concern for two reasons. Relaxed underwriting standards are in vogue even in this late stage of the credit cycle due to competitive pressures. In search of yield, asset managers too have continued to ramp up exposures to lower-grade loans and securities. In addition, risk monitoring structures remain stuck in old ways, hampered by resource, data and technology constraints. To be sure, many leading asset managers have invested in proprietary platforms to enhance credit risk surveillance. However, only a few firms have robust monitoring systems. Also, higher-end surveillance mechanisms are absent in the middle offices of even larger global banks.


Specifically, despite showing active interest, most banks are far from developing robust integrated early warning systems. Also, the existing data structures mostly operate in silos and do not tap non-financial data required for effective real-time monitoring. We believe that such gaps will hamper active, real-time credit surveillance by both lenders and fund managers, when the cycle turns.


We believe banks and asset managers will

  • Embrace automation judiciously: Teams will focus on automating credit assessments of low-to-medium default portfolios and blending automation with expert judgment for high-default, high-risk portfolios.
  • Deploy analytics to strengthen surveillance: Credit teams will build data analytics-driven tools (such as early warning systems), reinforced by a robust data mart that houses both traditional financial data and alternative datasets to generate actionable triggers on a real-time basis. We believe such steps will strengthen data aggregation and regulatory reporting as well as increase cost efficiencies.
  • Reallocate resources to riskier exposures: The headcount mix and effort of credit risk and research teams will get reallocated toward risky debt and weaker credits in light of a potential shift in debt ratings and volatility of credit markets. However, a successful realignment of teams will depend on the firms’ ability to a) tap automation and technology to free up time of teams; and b) expand in-house research capacity.