Successful implementation of AI requires a comprehensive and thoughtful approach to ensuring ethical, responsible, and compliant use.
We've developed this comprehensive questionnaire to assist financial institutions in evaluating their AI initiatives. This tool is designed to help organizations assess their current state of AI governance, identify potential gaps, and prioritize areas for improvement. By addressing the questions outlined in this questionnaire, financial institutions can establish a strong foundation for responsible AI adoption and mitigate the risks associated with AI-driven technologies.
Explore this guide:
View the presentation slide deck here.
- Strategic Alignment and Purpose
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- What specific objectives are we trying to achieve with this AI initiative?
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- How does the AI initiative align with our overall business strategy, risk management, and compliance objectives?
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- Are we using AI in areas where it will add value, such as improving operational efficiency, enhancing customer experience, or managing risk?
- Data Privacy and Security
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- What types of personal and financial data will be used in the AI models?
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- How are we ensuring that data collection and usage comply with privacy laws such as the Gramm-Leach-Bliley Act (GLBA)?
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- Are we obtaining the necessary consent from consumers for data use, and have we provided clear opt-out options where required?
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- What data security measures are in place to protect consumer information from unauthorized access, breaches, or attacks?
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- Are third-party data processors or vendors involved, and do they comply with our data privacy and security policies?
- Model Development and Implementation
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- Are AI models being developed according to a formal, well-documented process, including sound methodologies and data quality checks?
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- How are we ensuring that the data used in AI models is accurate, complete, unbiased, and representative of our customer base?
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- Do we have controls in place to ensure that AI models are only used for their intended purposes?
- Model Validation and Performance Monitoring
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- Are there independent and comprehensive validation processes for AI models before they are implemented and throughout their lifecycle?
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- How are we validating AI models' conceptual soundness, accuracy, and reliability, especially if they are complex or use machine learning techniques?
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- What techniques are being used to ensure the explainability and transparency of AI models, particularly in high-stakes areas like credit underwriting, fraud detection, and risk management?
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- How often are AI models monitored for performance degradation, bias, or inaccuracies?
- Model Governance Framework
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- Do we have a robust governance framework in place for managing AI-related risks, including clear roles, responsibilities, and accountability for model development, use, and validation?
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- Are there specific policies and procedures for managing AI models, including guidelines for transparency, interpretability, and periodic review?
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- Are we maintaining a comprehensive inventory of all AI models, including documentation of their purpose, design, validation, and use?
- Compliance with Regulatory Requirements
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- Are AI models in compliance with all relevant regulatory requirements, including the Fair Credit Reporting Act (FCRA), Gramm-Leach-Bliley Act (GLBA), and OCC SR 11-7?
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- How are we ensuring that AI models do not produce outcomes that could be considered discriminatory or in violation of fair lending laws?
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- What processes are in place to provide consumers with access to data and correction rights if their information is used by AI models?
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- Do we have adequate controls to prevent unauthorized or pretexting access to consumer information in compliance with the GLBA?
- Third-Party Risk Management
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- Are third-party AI tools or platforms used, and if so, do we have robust contractual obligations and monitoring processes to ensure compliance with our data privacy, security, and governance standards?
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- How are we validating, documenting, and monitoring third-party AI models to ensure they meet our internal policies and regulatory requirements?
- Bias and Fairness Considerations
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- How are we testing AI models for bias to ensure they do not inadvertently introduce or perpetuate discrimination?
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- What measures are in place to mitigate biases in AI models, including bias detection, correction, and ongoing monitoring?
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- Are we regularly auditing AI models to ensure they adhere to fair lending practices and other anti-discrimination laws?
- Explainability and Transparency
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- Can we clearly explain how AI models make decisions, especially those impacting consumers' financial opportunities (e.g., credit scoring, loan approval)?
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- What tools or frameworks are being used to enhance the interpretability of AI models (e.g., SHAP, LIME, Explainable AI techniques)?
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- Are we prepared to provide regulators and consumers with explanations for AI-driven decisions, if required?
- Data Minimization and Purpose Limitation
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- Are we collecting only the minimum amount of data necessary for the AI model to function effectively?
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- How are we ensuring that AI models are used only for their intended, disclosed purposes?
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- What controls are in place to prevent the unnecessary retention or use of personal data?
- Continuous Improvement and Adaptation
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- How will we continuously monitor AI models to detect performance degradation, bias, or other issues?
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- Are there processes in place for regular reviews, recalibrations, or updates of AI models to ensure they remain accurate, fair, and compliant?
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- What mechanisms are in place to learn from AI model performance and make necessary adjustments to improve their outcomes?
- Risk Aggregation and Reporting
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- How are we aggregating AI-related risks across the organization and reporting them to senior management and the board of directors?
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- Are there specific metrics or key performance indicators (KPIs) for monitoring and managing AI-related risks?
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- What is our approach to quantifying and communicating the risks associated with AI models?
- Consumer Communication and Rights
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- How are we informing consumers about how their data is used in AI models, and are we complying with the GLBA’s privacy policy disclosure requirements?
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- What processes are in place to handle consumer requests for information, access, or correction of data used in AI models?
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- Are we prepared to respond to consumer inquiries or complaints related to AI-driven decisions?
- Ethical Considerations and Social Impact
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- Have we assessed the ethical implications of our AI initiatives, including potential impacts on different consumer groups?
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- Are we taking proactive steps to ensure that AI models are designed and deployed in ways that are fair, transparent, and respectful of consumer rights?
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- How do we handle ethical dilemmas or conflicts arising from AI-driven decisions?
Next Steps and Actions
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- Based on the answers to these questions, what are the key risks, gaps, or compliance concerns that need to be addressed before proceeding with the AI initiative?
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- What additional resources, expertise, or tools are required to support responsible AI deployment within our organization?
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- Who is accountable for overseeing AI model governance, and what processes will be in place to ensure continuous compliance and ethical use?
View the slide deck presentation by clicking on the image below.