01
AI & Model Strategy
ML, deep learning and AI use-case design, model architecture review, comparative evaluation, deployment strategy and executive translation.
AI • Model Validation • Governance • Strategic Analytics
Build models that survive contact with reality.
Conquer Risk helps institutions turn AI, data and quantitative capability into systems that can be validated, governed, explained and used in high-stakes decisions. Our work spans finance, healthcare, telecommunications, manufacturing, technology and the public sector.
20+years in applied AI and model governance
1,000+ML, DL, AI and quantitative models built, validated or governed
~70institutional engagements
60+countries
$25Mcompetitive tenders led and won
From technical capability to accountable decisions.
The strongest model is not simply the one with the highest score. It is the one whose claim is clear, whose data support that claim, whose failure conditions are understood, and whose use can be governed in the real operating environment.
02
Model Validation & Assurance
Independent validation, calibration, bias and subgroup analysis, explainability, drift, monitoring, control design and challenge of model assumptions.
03
Data Strategy & Governance
Data lineage, decision frameworks, governance architecture, regulated data environments, operating controls and defensible external data sharing.
04
Clinical & Healthcare AI
Clinical AI, longitudinal and multimodal data, decision support, intervention-aware modeling, responsible deployment and healthcare interoperability.
05
Commercialization & Partnerships
Market propositions, technical proposals, competitive tenders, institutional partnerships, data products, IP strategy and translation of analytics into commercial value.
06
Team & Capability Building
Recruiting, training and leading quantitative, data, risk and AI teams; establishing operating standards and connecting technical teams to senior decision-makers.
Leadership
M. Antony Ewing, PhD
Conquer Risk was founded by M. Antony Ewing, a Princeton-trained quantitative economist, AI and data executive, former Group Chief Risk Officer and Chief Data Scientist, board-level technology executive, and current Professor of Research at the University of Illinois Chicago.
Across roughly 70 institutional engagements in more than 60 countries, he has built, validated and governed more than 1,000 ML, DL, AI and quantitative models and advised on more than $6 billion in client investments and initiatives. He led the technical proposals for and won approximately $25 million in competitive tenders.
Team leadership has been characteristic throughout his career. He has directly led more than 400 risk and data professionals, held broader executive responsibility for approximately 1,500 staff, and built and trained more than a dozen data-science teams.
AI and quantitative systems
20+ years across regulated and high-stakes environments, from enterprise model governance to current clinical AI and medical-data research.
Executive leadership
Former Group Chief Risk Officer and Chief Data Scientist; Executive Director and board member; founder and managing director of data and AI businesses.
Academic and research background
PhD and MA, Princeton University; BA with Honors in Economics and Mathematics, Northwestern University; former HKUST faculty member and Federal Reserve Bank of New York researcher.
Current medical AI work
Professor of Research at UIC and founder of Conquer Medical Health, with work spanning oncology, critical care, surgery, Alzheimer disease, ALS trials, imaging and population health.
Selected work.
The examples below preserve the substance of Conquer Risk's existing project history while presenting it in the language of AI, analytics, governance and enterprise transformation.
Digital Strategy • China
Digital-bank and fintech architecture for major state institutions
Advised large Chinese financial institutions responding to fintech and platform disruption, including digital-bank architecture, mobile ecosystems, underwriting, risk management and strategic situation-room design.
Technology Governance • Europe
Enterprise technology-risk framework
Performed gap analysis, benchmarking and framework design for a major Central and Eastern European institution, connecting technology risk, regulation, governance and senior management oversight.
Model Validation • Scandinavia
Model assurance and data-science team development
Trained and led quantitative and data-science teams in model validation, calibration, challenge and the communication of model implications to executives and boards.
Predictive Analytics • Africa & Asia
Mobile lending and multi-channel decision systems
Recruited and led data-science teams developing predictive analytics, underwriting and decision architecture for mobile savings, lending and digital-channel systems at national scale.
Selected institutional and advisory relationships.
Expert-advisory and successful-bid work has included Goldman Sachs, leading strategy and professional-services firms, and major technology and financial institutions.
Goldman Sachs
McKinsey & Company
Boston Consulting Group
PwC
KPMG
EY
IBM
Microsoft
Oracle
Birlasoft
AXA / AXA Rosenberg
Research: when models should be trusted.
The research program asks a prior question to model performance: does the available evidence contain the information required for the claim? It spans intervention-aware AI, information ceilings, transportability, scientific reproduction, biological trajectory analysis, and systemic AI risk. The full medical-AI portfolio is maintained at Conquer Medical Health.
Research Square • DOI
Intelligence Breaks the System It Controls
Shows how intervention can deform the relationship on which both clinician and model rely, creating shared informational blind spots.
Research Square screening • Available upon request
Intelligence Preserves the System It Assists
The constructive companion: when information survives but is unevenly available, selective augmentation can allocate action to the observer that still carries signal.
Research Square screening • Available upon request
Intelligence Cannot Recover What the Instrument Does Not Record
Measures information ceilings, acquisition latency, transport failure and selective prediction in optical neural measurement.
Research Square • DOI
AI on Alzheimer Disease
Uses field-scale computational reproduction to test whether major Alzheimer hypothesis families are distinguishable on common data.
Research Square • DOI
Information Limits Constrain Medical AI Claims Beyond Model Sophistication
A predeployment assurance framework for separating estimator weakness from information insufficiency in the substrate.
SSRN • DOI
Nash Drift
Examines representational closure and strategic interaction when actors increasingly make decisions through shared model representations.
SSRN • DOI
The Systemic Risk of AI
Frames systemic exposure created when institutions depend on common frontier AI providers and model infrastructure.
Completed manuscript • Available upon request
Prediction Error Masquerades as Individual Treatment Response
Uses placebo-controlled ALS data to distinguish patient-level response claims from ordinary prediction error.
Writing, teaching and public thought leadership.
More than 80 public articles and essays have appeared in Harvard Business Review, Forbes, Inc. and other outlets, alongside graduate teaching in applied analytics at Columbia University.
Harvard Business Review • Risk Management
How to Reverse-Engineer Criticism
Using external and internal criticism as structured information for organizational improvement.
Harvard Business Review • Management
Cutting Costs Without Cutting People
A leadership approach to cost discipline that preserves institutional capability rather than defaulting to layoffs.
Harvard Business Review • Strategy
7 Remedies for a Bad Strategy Hangover
Distinguishing a one-off decision error from a structurally flawed strategy and responding accordingly.
Forbes • Leadership Analytics
4 Leadership Metrics You Can't Afford to Ignore
A quantitative approach to leadership effectiveness and behavioral measurement.
Forbes • Risk & Resilience
Top 10 Risks of 2021 and How You Can Manage Them
Risk, resilience and strategic adaptation under fast-changing operating conditions.
Forbes • Technology Governance
Dangerous And Hidden Microsoft Feature Could Destroy Your Career And Your Business
On workplace measurement, behavioral scoring, and the governance risks created by apparently useful technology.
LinkedIn • 2024
Dear LinkedIn Marketers: Please Stop 'Marketing'
A current essay on trust, communication, and the difference between relationship-building and formulaic outreach.
Contact
Bring the model, the data, and the decision into the same room.
For executive advisory, AI and model validation, governance, data strategy, technical proposal support, clinical AI, or institutional capability building, contact M. Antony Ewing.