RECOGNIZED EXPERT IN THE MANAGEMENT OF ANALYTICS
As a renowned analytics expert, C-suite advisor, and now trusted thought leader, Chris’s expertise lies in helping global organizations ensure effective and sustainable management of their analytics, through robust oversight governance.
Chris previously co-led McKinsey's Model Risk Management service line. Since 2024, he is a Model Risk Management Executive at TD Bank Group.
Toward a New Baseline for Transformative Change: Strategic, Responsible, and Sustainable Solutions for Analytics
Having co-led McKinsey's Model Risk Management service line, and spearheading multiple transformative programs for global institutions, Chris supported organizations and professional partners across the US, Canada, and Europe with the intricacies of AI adoption and analytics management.
From public speaking and executive coaching, to optimizing model lifecycle activities and enhancing AI governance frameworks, all of Chris’s previous experience supports a responsible, purpose-driven, and effective approach to transformative change.
Model Lifecycle Transformation
Chris is a highly-regarded model lifecycle transformation expert, well-versed in addressing model risk-related issues and streamlining model lifecycle activities. His ability to enhance policies, raise standards, and optimize procedures with minimal disruption to operations ensures efficient and effective model lifecycle management.
AI Governance
Chris also excels in developing, reviewing, and reinforcing AI governance frameworks. Drawing on extensive industry knowledge, Chris ensures regulatory compliance while aligning AI strategies with organizational goals. From aiding Top-8 US banks in regulatory exam preparation to validating GenAI solutions, Chris's emphasis on ethical practices and risk management places trust and accountability at the center of AI adoption.
End User Computing Tools Governance
Chris has become the go-to strategic advisor in the development and execution of pragmatic End User Computing Tools (EUCT) policies, procedures, and templates for global institutions. With a keen understanding of regulatory requirements and industry best practices, Chris ensures all EUCT processes are governed effectively, minimizing risks and optimizing operational efficiency.
Model Validation
Chris’s expertise in model validation ranges from capital planning to cyber risk model validation. Identifying, assessing and mitigating risks across a diverse range of use cases and modeling techniques (qualitative, statistical, and AI methods), Chris thoroughly validates each model with meticulous attention to detail and a deep understanding of industry-specific standards. This approach helps organizations meet regulatory requirements and deliver accurate results with an independent partner.
Technology
Chris is renowned for his expertise in designing effective automation approaches and workflow management tools for analytics. From implementing new MRM Policy requirements in model inventory systems to designing automation platforms for model validation activities, Chris uses technology effectively to optimize operations and raise operational standards — all while maintaining regulatory compliance and driving strategic goals.
Capacity & Capability Management
Chris leverages his deep interest in strategic remediation, model onboarding, and compliance transitions to support coaching executives in their transition to new roles. Designing and deploying custom analytics training programs and capacity plans across model lifecycle activities, Chris’s agile approach to organizational challenges ensures global organizations can develop comprehensive reporting processes, drive sustainable growth, and ensure effective talent management.
CEO of a Global Business Technology Services Company
Head of Model Risk Management of a Top-10 US Bank
CEO of a Boutique on Macroeconomic Forecasting
Head of Model Risk Oversight of a European Bank
Explore Chris’s Latest Publications
Navigating economic uncertainty: New guidance for credit risk management
Amid persistent complexity, bank leadership teams need to urgently revisit their approaches to credit risk management.
Using model risk management to address climate analytics: It’s a process, not a task
Climate models create significant risks and complexity. Model risk managers need a tailored approach to ensure they are fit for purpose.
The fight against money laundering: Machine learning is a game changer
To realize the full benefits of machine learning and advanced analytics in anti–money laundering, institutions need AML experts, strong data science talent, and reliable data sources in the fight against this type of financial crime.
Perspectives on model risk management of cybersecurity solutions in banking
The sooner banks start their journey and establish an effective approach to model risk management of cybersecurity solutions, the quicker they will be able to manage risk and establish controls.
Model risk management 2.0 evolves to address continued uncertainty of risk-related events
Organizations this year plan to enhance their MRM framework capabilities—including risk culture, standards, and procedures—and to upgrade their validation resources with MRM 2.0 firmly on the agenda.
Transforming the end-to-end model life cycle through digitization
With increasing global complexities, those financial institutions that take a more integrated, strategic approach to model life-cycle management can unlock massive potential.
A strategic vision for model risk management
The expanding scope of models and the increased use of models based on advanced analytics have amplified the strategic importance of model risk management.
Scaling analytics across financial services: Understanding the value of model life cycle transformations
To address the challenges of scaling up analytics business areas, financial institutions can accelerate value delivery with four efficiency levers.
Managing the risks and returns of intelligent automation (cited in Forbes)
As companies accelerate the adoption of automation and artificial intelligence, they need a better way to maximize the benefits of these new technologies while minimizing the risks.
An AI-first approach to model risk management
This article sets out practical considerations for setting up an effective AI risk management governance framework
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Upcoming Events
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