There is a very interesting discussion on stackexchange on how to determine the credit risks of a startup.
What would be the ideal way to develop the IFRS9 ECL model for startup fintech when there is no historical data.
There are 2 answers to this question (as of November 2021)
- This is more of an educated guess than an actual answer, I may be completely off track – take this at a discount: I would identify and quantify my targeted clientele („EUR <region> retail: x %, EUR <Region> corporates y %…“) and I would then buy default data / credit histories / model parameter from data vendors. Another idea could be to reach out to companies that offer outsourcing for risk models / model estimates, at least during the initial phase of your product.
- According to the regulations of IFRS 9, the valuation for the ECL is not permitted exclusively on the basis of historical values. (For example, IFRS 9.B5.5.17). The inclusion of macroeconomic data and the economic environment is also desired. Similar companies can also be included in the valuation.
This is in fact a very tough question. We have faced this situation frequently in our consulting practice. To determine the credit risks of a startup, or a private company in general, we usually utilize
- Data of comparable public companies,
- The startup’s recent debts,
- Relevant high yield credit indices,
- A structural credit risk model,
- Combination of the above.
Another possible solution is to develop a predictive model, but again, lack of data will be an issue.
Let us know what you think.
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