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Integrating Climate in Credit Risk Modelling - USA

Quantifying climate risks and incorporating them into traditional credit risk models

A beautiful view of Shanghai city from a window with a glass building in the front

A half-day course

Conceptual foundations for climate-driven credit risk modelling

  • Climate change and financing
  • How to define an ontology of credit-relevant climate risk factors
    • Event risks
    • Outcome risks
    • By product
    • By sector
    • By geography
  • Epistemic and data considerations: from judgmental modelling to statistical modelling of climate risks
    • Judgmental credit risk modelling, aligned with current ECB, EBA, and PRA regulations
    • Statistical credit risk modelling, currently under development at commercial banks

Climate risk assessment and its impact on credit risk

  • Physical climate risks assessment methodologies
    • Vulnerability assessment: exposure, sensitivity, and adaptive capacity for physical risks
    • Sensitivity assessment
    • Value at risk for physical risks
    • Climate stress tests for physical risks
  • Transition climate risks assessment methodologies
    • Vulnerability assessment: exposure, sensitivity, and adaptive capacity for transition risks
    • Sensitivity assessment
    • Carbon accounting
    • Green/brown ratio
    • Value at risk for transition risks
    • ‘Temperature’ (ie Paris alignment)
    • Climate stress tests for transition risks
  • Climate risk transmission and its impact on PD, LGD, and EAD
    • From physical risks to PD, LGD, and EAD
    • From transition risks to PD, LGD, and EAD
    • From climate risks to non-credit supervisory risks, to credit risk

Integration of climate risk in credit risk modelling

  • Integration of climate risk factors into shadow PD models:
    • Data preparation
    • Variable selection
    • Model development
    • Model validation
    • Calibration
  • Integration of climate risk factors into shadow LGD models:
    • Data preparation
    • Variable selection
    • Model development
    • Model validation
    • Calibration
  • Integration of climate risk factors into credit scorecards
    • Sector tilts
    • Geographic tilts

Our credit risk modeling course trainer has 15 years of experience in the banking sector. She has worked across credit risk and risk analysis, including climate scenario analysis and stress testing for banks such as KBC, OTP and Raiffeisen Bank. At KBC, she was a senior risk manager, leading in developing and implementing credit risk management toolkits for measuring and monitoring ESG/climate risks and opportunities, and reporting those to the Executive Committee. Notably, she performed transition and physical climate risk materiality assessments based on scenario narratives, implemented PACTA methodology for the loan portfolio and contributed directly to the European Central Bank’s climate stress test by the firm.

More recently, she delivered consultancy, in the role of Principal Consultant on the topics of ESG and credit risk; she also established the climate and environmental risk management services offering for a consultancy firm.

She holds an MSc in Finance and Actuarial Sciences, a PhD, and a specialization in Corporate Finance from the Corvinus Management and Business Administration Doctoral School in Budapest. She is also a certified financial analyst and certified risk manager (CFA – Chartered Financial Analyst, FRM – Financial Risk Manager, PRM – Professional Risk Manager), and also obtained the GARP Sustainability and Climate Risk (SCR) Certificate.

  • To have a thorough understanding of the conceptual foundations of climate-driven credit risk modelling.
  • To apply materiality assessment at a company level and portfolio level for the impacts on the credit of physical and transition climate risks.
  • To understand how physical and transition risks transmit into credit risk metrics such as PD, LGD, and EAD.
  • To comply with current regulatory requirements and/or recommendations.
  • To integrate climate factors into credit risk modelling for PD and LGD.
  • To integrate climate tilts in credit risk scorecards.

Our credit risk modeling training course will give you an insight into the following:

  • Focus on the quantitative approaches to the financial materiality that ESG risks can have for credit risk.
  • The practical aspects of adapting financial models.
  • A thorough review of related academic research and its implications.
  • Perspective based on international experience in ESG.

Our credit risk modeling course is a 'must-know' for:

  • Credit analysts
  • Risk officers
  • Treasury professionals of corporates

The jury is still out on whether and to what extent climate-related risks are already incorporated into credit pricing and other financial indicators and their models. The reality is that the physical impacts of climate change and the need to adapt to increasing climate-related regulation are currently having impacts on business models, revenue, costs, and other business factors. This applies both to financial institutions as well as real economy businesses. To date, little explicit integration of climate factors in credit risk modelling has taken place by financial institutions, and financial supervisors are applying pressure to change that.

Our credit risk modeling training course helps participants to distinguish between the underlying concepts of climate-change-driven credit risks, and navigate the limitations of currently available practices such as data availability. Participants will identify the risk transmission channels that lead from climate change to financial impact on corporates. Finally, our credit risk modeling course takes participants through the steps to practically integrate these risks into existing credit risk models.


Have this course presented In-House

  • On a date, time and in a location of your choice
  • Topics expanded or deleted to your bespoke requirements

Have this course pre-recorded

  • Full course recording edited exclusively for your company
  • Files converted to enable housing on your LMS
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