AI is transforming the financial system. A recent global
Why AI evaluation matters
As with all moments of immense change, generative AI comes with great and growing responsibility for boards and C-suite executives. Risk management systems will either by law or best practice require an AI category for risk mitigation. Those companies responsible for governance and want to be ahead of the regulatory curve need to understand how AI is deployed, used, and managed internally and externally. The buck ultimately stops with the board - but are boards truly ready for this new and growing responsibility or, as some describe, liability?
Anecdotally, boards are either fearful of what is ahead or curious about the opportunity that is emerging. Despite increasing legislation, including the
The need for keeping humans in the loop
To mitigate risk and harness the promise of AI, a fundamental best practice is to evaluate and assess AI systems or tools before using them. This will help businesses ensure trust, mitigate future risks, and adhere to compliance. While it's simpler to manage when AI is developed in-house, if an external partner is needed, thorough due diligence is essential before purchasing any AI external system or tool.
This process should also involve human oversight for verification and reassurance. Recently there has been lots of discussion about
Identifying AI's potential risks
Other issues to prioritize are biases in training data and algorithms that can lead to unfair or discriminatory outcomes, which is particularly concerning in domains like insurance underwriting. Businesses must implement techniques like bias testing, fairness constraints, and diverse data sampling to identify and address potential discrimination in their AI models. If you can't explain these models to a regulator or even a customer, you should be careful using them at all. AI models can be vulnerable to adversarial attacks, data poisoning, or prompt injection, compromising their integrity and reliability. Thorough testing and evaluation such as 'red teaming' can help test an AI model's robustness, particularly in mitigating harms.
The path to AI compliance
Risk analysis and mitigation should not be taken lightly. It needs to be completed in a comprehensive way, taking into account the various factors that constitute an AI model. This includes visibility, transparency, integrity, optimization, effectiveness, and legislative readiness. Adopting this all-inclusive approach is essential for determining whether an AI model can be relied upon to yield optimal outcomes without posing any societal harm.
The financial services industry is already subject to numerous regulations and compliance requirements, such as data privacy laws, financial reporting standards, and industry-specific guidelines. Businesses must assess their AI models' compliance with these regulations and ensure their use aligns with legal and ethical principles. Beyond ethical ramifications, there might also be legal fines to consider as a consequence of failing to comply - an avoidable cost to any business.
We stand at the dawn of a new era where AI is changing our world and every business. To fully harness the opportunities AI presents, and trust the outcomes, companies must consider their risk appetite, partners, and ultimately understand their customers' attitudes toward AI. With so much fearfulness, rightly or wrongly, some customers will want to know how involved an AI has been in decisions that impact their individual rights. C-suites and boards will require a vigilant eye to harness the potential that AI brings, and the clear associated risks.
