Dr. Mays has led large-scale model development and oversight functions for some of the largest banks in the US, with responsibility for overseeing credit risk evaluation and decisioning models and fraud detection models used in high-stakes financial decision-making.

Dr. Mays has deep expertise in evaluating the design, performance, and limitations of statistical and machine learning models, including the appropriateness of methodologies, data integrity, feature selection, and model validation practices. She is particularly experienced in assessing whether model outputs are reliable, reproducible, and supported by sound empirical evidence, as well as whether stated model capabilities are consistent with underlying methodologies and results.

Her work has included building and overseeing independent validation frameworks aligned with banking regulatory guidance, with a focus on identifying model weaknesses, unsupported or inappropriate assumptions and methodologies and deficiencies in documentation or model testing and monitoring. She has also advised on the risks associated with vendor-provided models, including the evaluation of model transparency and performance claims.

Dr. Mays is known for her ability to analyze complex technical materials—including model documentation, research papers, and public disclosures—and translate them into clear, evidence-based conclusions regarding model performance, limitations, and risk. Her recent work has focused on the governance of AI and machine learning models, including the evaluation of claims made about their capabilities in consumer lending, fintech platforms, and other data-driven applications.

She advises financial institutions, fintech companies, and others on predictive model development and evaluation practices and model and AI governance frameworks used in business decision-making.