1. LLMs in general are not build for quantitative analysis. Loan-to-value, income ratios, etc are not supposed to be calculated by such a model. Possible solution would be to calculate this beforehand and provide it to the model or train a submodel using a supervised learning approach to identify good/bad
2. Lending models are governed quarterly yet see relevant cohort changes only after a period of time after credit decision which can be many years. This prompt above does not take this performance of the cohort into consideration
3. Based on the governance companies adjust parameters and models regularly to adjust to changes in the environment. I.e., a new car models comes out or the company is accessing a new customer segment. This process could not be covered well with this prompt since there would be no approvals/ denies for this segment.
4. Since transfer of personal-identififation data needs to be consented, it would likely be necessary to host an LLM like this internally or find a way to ensure there is no data leakage from the provider to other users on the platform.
5. Credit approval limits are not necessarily covered by this proceess. I.e., the credit decisions is unclear but would work with 5-10% more downpayment. Or the customer would be asked to lower the loan value or find someone in the company who can underwrite that loan volume. This person then has usually a bunch of additional questions (liquidity risk, interest risk ,etc) to ensure that the company is well protected and the necessary compliance checks are adhered to.
6. The discussions about this with regulators and auditors will be entertaining.
Yet, I think it IS an elegant prompt which might provide some insights.