Artificial intelligence is transforming every industry, from finance to healthcare to education. As organisations increase their reliance on AI models, the accuracy, robustness, and reliability of AI generated models and processes become critical. As AI becomes increasingly embedded in workflows, failures can cause massive operational losses, regulatory breaches, and reputational damage.
At Genius Mathematics Consultants, we specialise in independent model validation services, AI model audit and AI risk management. We analyse, test, and certify AI systems and AI generated work to ensure that they behave correctly, consistently, and safely.
What Is AI Model Validation?
The capabilities of AI are truly impressive, and improving every day. Yet, all of us have experienced AI producing work that contains mistakes. This is simple a result of the fact that current generation AIs simply generate text that is “likely” to be true, based on the data it’s been trained on. While the latest AI models do attempt to incorporate logic engines that should help reduce this, their accuracy can still fail in critical ways.
In many industries, particularly financial services, careful model validation by expert quantitative staff has long been a necessity. But the capability of AI to rapidly generate plausible but not always reliable work expands this requirement by an order of magnitude. Effective AI governance thus requires that all AI generated work be carefully checked and verified. But what is the fastest and most efficient way to do this?
How to validate AI models
Validating AI generated work requires a multi-layered approach, consisting of at least these steps:
- Review of the methodology the AI has proposed
- Manual inspection of the code to ensure faultless agreement with the proposed methodology
- Benchmarking the model against independent benchmark models to check for agreement within some tolerance
- Checking the behaviour of the model for all qualitatively different cases, including rare, unusual “stress scenarios”, to ensure it behaves as expected under all scenarios.
- Passing the AI generated work to alternate AIs for independent checking. It’s less likely that multiple AIs will make the same mistake. As always, it’s good to give the AI specific instructions on how to check the code, to increase the quality of the result.
A simple case study
This case study concerns option pricing models in quantitative finance, but the principles extend to validating many kinds of AI generated work.
For a recent project we needed to build a Monte Carlo model in C# to price financial options. We needed the model to be able to handle early exercise, barrier and Asian option variants. The model used the Longstaff-Schwarz method to handle early exercise. To build this code manually might have taken a number of weeks. Using AI, it took 1-2 days.
To validate, we set up a large number of quite comprehensive unit tests to validate the code against independent models. In addition to the required MC code, we had the AI generate a number of auxiliary models to benchmark the Monte Carlo code against. The pricing of barrier options could be checked against the closed form Black Scholes barrier equations, the pricing of American options could be checked against a binomial tree model, and the pricing of Asian options could be checked against the method of moments. We also used AI to generate the comparison models. Although we were checking AI generated code against AI generated code, as the comparison models are conceptually very different to Monte Carlo, the chances of both pieces of code being wrong in the same way is very small.
Secondly, we set up a second set of unit tests for stress testing and edge-case testing. This included a range of unit tests where the correct output of the code is obvious. For example, an already knocked-in option should have the same price as a vanilla option, an American call is never optimal to early exercise, and so on.
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