Climate Risk Stress Testing and Reporting
Data Preparation and Input
- The Solution UI accepted data files in a pre-specified template, which includes the sector, revenue, EBITDA, market value, debt, credit rating, and carbon emissions (Scope 1, 2, and 3) and any other relevant fields.
- The Solution provided this template to the model user.
- The Solution highlighted discrepancies, if any, that need to be resolved.
- Proxies of missing data: The data template contained a spreadsheet with data transformation tools to assist in finding proxies for missing data in each company’s dataset if required:
- Mapping Market Value: Average industry revenue multiples in the region or book value
- Mapping Carbon Emissions: A proxy measure based on industry-level emission multipliers will be used for mapping carbon emissions. These multipliers represent the amount of CO2 released into the atmosphere from the combustion of both Scope 1 and Scope 2 fuels per million USD of output.
- Any other Central Bank of UAE (“CBUAE”) and dataset-specific transformation requirement
Data Validation
- The Solution provided an ETL Module system to manage the data loading and validation process after the Client uploads their data.
- If the data fails any validation checks, the UI system reported the errors in a log file for the user to review the errors, correct the data, and re-upload the corrected spreadsheet.
Stress Testing and Output
- Upon successful data upload and passing the data validation process, our Solution initiated the climate transition risk bottom-up analytics model.
- ALP conducted initial enhancements and modifications to the CSV output generation process and the UI when required.
- Our system currently supports a stress testing approach to assess the financial impact of carbon emissions on borrower companies in line with CBUAE guidance.
- By integrating regional carbon prices that vary across different climate scenarios, the model calculated a carbon cost for each company based on its reported emissions.
- This calculated carbon cost was then factored into the company's financial metrics, specifically affecting EBITDA and subsequently the total asset value.
- This reduction in asset value was fed into the Bank's internal rating model to obtain projected loan ratings and credit risk measures such as Probability of Default (PD) and Loss Given Default (LGD).
- The system generated a detailed output page on the UI, summarizing the results of the climate transition risk analysis. The output will include key financial impacts and metrics, providing a comprehensive view of the assessed risks and impacts.
- All results were saved in a spreadsheet that conforms to the CBUAE template requirements.
Physical Risk Reporting
Data Collection and Validation for Reporting to CBUAE:
- ALP conducted initial enhancements and modifications to the CSV output generation process and the UI when required.
- The refined data was securely stored in a database, ensuring it is well-prepared for future CBUAE physical risk stress tests.
- Currently, CBUAE reporting requires only data collection of obligors, collateral, geo-positions etc. There is no requirement for banks to calculate the impact of hazards on the LTV, LGD and RWA.
- The current system supported some of this functionality but when more clarity on such calculations is obtained from CBUAE, this functionality can be turned on and calibrated/customized to produce the required results.
Support
- Comprehensive documentation and user guides were provided to assist the Client from end to end. Support services were available to address any queries or issues related to the data requirement, ETL system and validation tasks, and UI outputs.
- Any support query once the system is in production could be raised and resolutions were provided within 48 hours.