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




We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

5 Steps to correctly prepare your data for your machine learning model.

The era of business intelligence and its benefits

Classifying Character Classes in Dungeons & Dragons With Machine Learning

Go to my profile and see how future events can be predicted.

Trading Strategy and Stock Price Prediction for Boeing

Data Governance Models and the Environmental Context

5 Lessons I Learnt from A Kaggle Competition

Tableau vs Google Data Studio : Titan vs the New Comer!

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Acuity Knowledge Partners

Acuity Knowledge Partners

We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points.

More from Medium

Graph Data Structure And Its Applications

Michal Liska: the man of many talents

View from the Summit: How the United States Department of the Air Force Transitioned to a…

Learning curve and its price tag