ESG Risk Scoring
Identification of ESG risks
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The supervisory authorities require adequate consideration of ESG risks in banks' risk management. ESG risks should be taken into account like any other significant risk and integrated into the processes. This affects, among other things, the calculation of the IFRS 9 expected credit loss, the calculations of internal rating-based models (IRB) and pricing in the lending process.
To calculate the individual ESG risk scoring, Climcycle uses a variety of data sources on physical risks, transition risks, social risks and governance risks. In addition, the platform allows
its users to customise the underlying methodology according to their individual specific circumstances.
Input & Output
All data can be uploaded as Excel or via API or entered manually directly in the user interface.
All input data is taken into account and linked to corresponding data sources. The results are completely transparent and can be analysed either directly in the user interface or in Excel.
Methodology
Climcycle combines input data with external data sources to calculate the relevant ESG risks. All data sources used are selected based on various criteria such as relevance, coverage and differentiation. Climcycle is guided by the recommendations of the supervisory authorities and best practice examples.
Flexibility
We offer the greatest possible flexibility in customising the scoring methodology. Users themselves can decide which data is used, which assumptions and weights are used for the calculation, and how the final results are compiled. This enables customised scoring, allowing users to set their own priorities with regard to the ESG risks under consideration.
Coverage
The combination of different data sources enables the ESG risk scoring module to cover all countries in the world and all industries (at NACE code level). The industry classification according to the NACE code ensures simple and standardised data input by the financial institutions on the one hand and provides clearly differentiated coverage of all industries on the other.
Differentiation
In order to perform reliable analyses at deal level, Climcycle uses data from banks and external data sources that are as differentiated as possible.
If no individual transaction data
available, Climcycle uses average data for sectors and regions. New data sources are added regularly and existing sources are updated at least annually.
Award
SAS Hackathon 2021 Award for the EMEA region (Europe, Middle East, Africa) for the use of artificial intelligence in ESG risk management.
Use Case
Identification and quantification of ESG risks
Climcycle uses simple input parameters, which are recorded during the lending process, to identify potential ESG risks as a first step. These analyses are used to identify potentially vulnerable portfolio clusters, e.g. a larger sub-portfolio in a flood zone or a CO2-intensive sector. In the second step, these risks are quantified using damage functions, i.e. translated into monetary effects. These results are used in particular for consideration in the credit conditions, in the IFRS 9 ECL and, in the future, also in the IRB models.
