Introduction

Model Cards—standardized documentation of model capabilities and limitations—improve transparency and enable informed deployment. For financial models, cards should document: intended use, training data, performance metrics, bias findings, deployment considerations. Cards facilitate regulatory compliance and internal governance.

Model Card Components

Model Details: name, version, date, intended use. Data: training set characteristics, size, potential biases in data. Performance: accuracy across demographic groups, performance under distribution shift. Ethical Considerations: known biases, fairness limitations. Recommendations: appropriate use cases, inappropriate use cases, deployment safeguards.

Creation Process

Develop during model development, not post-hoc. Update when model changes or new findings emerge. Include input from data scientists, ethicists, compliance. Review before deployment. Maintain as living document.

Benefits

Transparency to stakeholders. Institutional knowledge preservation. Regulatory documentation. Prevents misuse by clearly stating limitations. Facilitates responsible AI governance.

Conclusion

Model Cards are essential governance artifacts enabling responsible, transparent deployment of financial ML.