Case Study | AI Model Validation Across Global Markets at a Tier-1 IB
Functional Topic
AI Model Validation β Financial Risk & Compliance
Project Industry
Investment Banking / Financial Services
Needed Support
AI Risk & Compliance Consultant
Duration
2 Months
The Challenge
A leading Tier-1 global investment bank required expert guidance to validate a suite of newly developed AI models used for trading and risk assessment across its global markets division. With increasing scrutiny from regulators in the US, EU, and APAC regions, the bank needed to ensure its models met the highest standards of compliance, transparency, and performance.
The models in question leveraged machine learning for tasks such as trade surveillance, pricing optimization, and liquidity forecasting. However, the rapid pace of internal development had outpaced governance controls, and the internal model risk team lacked experience with advanced AI techniques.
Key challenges included:
- Navigating AI validation in line with global regulations such as SR 11-7, EU AI Act, and MAS guidelines
- Validating opaque AI and machine learning models with limited explainability
- Bridging knowledge gaps between quantitative developers and compliance stakeholders
- Creating a scalable, future-ready AI model validation framework
To address these issues, the bank sought a consultant with deep expertise in AI model governance, experience working in highly regulated financial environments, and the ability to operate across cross-border regulatory regimes.
Role of Consultport
To find an expert who could lead this critical AI validation initiative, the client turned to Consultport, which swiftly proposed a shortlist of 4 AI consultants. After a round of interviews, the client selected a consultant who brought:
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7+ years of experience in quantitative finance, AI, and model risk governance
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Prior leadership roles at global banks and regulatory bodies
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Deep familiarity with SR 11-7, PRA, BaFin, and MAS model requirements
The consultantβs combination of technical rigor and regulatory insight made them the ideal fit to lead the validation effort and guide the bankβs compliance strategy.
The Approach
The consultant designed a comprehensive AI model validation strategy focused on regulatory compliance, model integrity, and long-term scalability. The work was structured across four main pillars:
1. Validation and Regulatory Gap Analysis
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Assessed the maturity of existing processes and documentation
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Identified key gaps compared to regulatory expectations, including ECB, PRA, and BaFin guidelines
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Aligned validation efforts with internal audit and enterprise risk management
2. Model Evaluation and Explainability
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Reviewed AI model performance, robustness, and drift detection methods
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Applied best-in-class AI model validation tools such as SHAP and LIME to ensure model transparency
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Documented explainability approaches to meet regulatory scrutiny
3. Governance and AI Model Validation Framework
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Established a formal AI model validation workflow, with defined checkpoints, roles, and KPIs
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Integrated AI risk controls into the bankβs broader model governance architecture
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Delivered templates and toolkits for ongoingΒ monitoring
4. Stakeholder Alignment and Reporting
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Led cross-functional workshops to ensure global consistency in AI validation standards
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Delivered regular updates and final validation reports to senior stakeholders and regulators
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Created a global playbook for future AIΒ efforts across business units
The Results
At the end of the project, the client had important advancements in validating its AI models across global market divisions:
Enterprise-Grade AI Model Validation
All priority AI models passed validation and were approved for production in global trading operations.
Regulatory Readiness
The bank strengthened its position with regulators by implementing a consistent, auditable process across all key markets.
Scalable AI Validation Framework
A reusable AI validation model was delivered, enabling efficient rollout for future AI implementation initiatives.
Global Governance Alignment
Improved collaboration between regional risk teams led to a unified approach,Β reducing friction and boosting compliance confidence.
Weβre happy with the work of the consultants! He helped us build a robust framework, even exceeding initial expectations.
βΒ Global Head of Risk & Model Governance
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