Pandemic-induced micro transformations in credit analysis and credit risk management

  • Increasing borrower- and portfolio-level analysis, with in-depth assessment of the pandemic’s impact on business performance, business strategy, industry outlook and underlying risks
  • Growing focus on building more variables into credit risk models while keeping a check on the traditional probability of default (PD) and loss given default (LGD) models
  • Building multi-pronged and multi-weight forecasts based on cash flow, liquidity, recovery time frame and revised budgets
  • Developing comprehensive sector-specific key risk indicators based on sectoral trends and exposures
  • Increasing emphasis on qualitative factors such as recent events, corporate actions and related news on the industry and company
  • Frequent monitoring of data on liquidity of sectors more vulnerable to demand shocks due to a crisis
  • Increasing monitoring requirements from once a year to once a month/quarter due to an uptick in risk rating downgrades and significant portions of the portfolio being downgraded to stressed credit levels
  • Close monitoring of covenants, with a sharp focus on credits with deteriorating headroom, and incorporating a watch list to track sharp periodic declines in performance
  • Immediate/instant reporting of breaches and loans newly added to the watch list
  • Using artificial intelligence/machine learning technology to collect and feed data to accelerate the decision-making process
  • Meeting ever-increasing demand for faster credit approval while conducting critical credit quality checks to meet urgent liquidity requirements during economic crises
  • Centralising and standardising data infrastructure and building links between all internal and external data sources
  • Adopting a risk-based underwriting approach for non-bespoke products (more prevalent in the middle market and business banking space)
  • Direct processing by building automated underwriting systems (primarily for retail loans) by complementing data generated from in-house banking systems with third-party offerings
  • Using alternative credit data for improved risk decisions. Making this data available through application programming interfaces (APIs) helps in almost-real-time input of information critical for underwriting decisions
  • Minimising defaults by proactive portfolio monitoring — developing early warning systems with tailor-made, sector-specific triggers and headroom indicators, current and predictive credit scoring and remediation strategies
  • Most importantly, building excellent relationships within the client’s credit, underwriting and monitoring teams, reducing lead time in decision making

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We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points. https://www.acuitykp.com/

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Acuity Knowledge Partners

Acuity Knowledge Partners

We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points. https://www.acuitykp.com/

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