Underwriting, lending and insurance premium calculations are some examples of where the financial and insurance industries need to keep a watchful eye for data bias.
As we feed more data into LLMs and AI technologies, the noise and data biases will interfere with the final output. Businesses should go deeper than bigger to get the most out of their enterprise data.
Having the right policies in places can help combat bias in AI/ML decisioning. Follow these practices to craft an ethical automated decisioning solution.