AI & Model Strategy
ML, deep learning and AI use-case design, model architecture review, comparative evaluation, deployment strategy and executive translation.
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.
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.
ML, deep learning and AI use-case design, model architecture review, comparative evaluation, deployment strategy and executive translation.
Independent validation, calibration, bias and subgroup analysis, explainability, drift, monitoring, control design and challenge of model assumptions.
Data lineage, decision frameworks, governance architecture, regulated data environments, operating controls and defensible external data sharing.
Clinical AI, longitudinal and multimodal data, decision support, intervention-aware modeling, responsible deployment and healthcare interoperability.
Market propositions, technical proposals, competitive tenders, institutional partnerships, data products, IP strategy and translation of analytics into commercial value.
Recruiting, training and leading quantitative, data, risk and AI teams; establishing operating standards and connecting technical teams to senior decision-makers.
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 model-dependent decisions. 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.
20+ years across regulated and high-stakes environments, from enterprise model governance to current clinical AI and medical-data research.
Former Group Chief Risk Officer and Chief Data Scientist; Executive Director and board member; founder and managing director of data and AI businesses.
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.
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.
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.
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.
Performed gap analysis, benchmarking and framework design for a major Central and Eastern European institution, connecting technology risk, regulation, governance and senior management oversight.
Trained and led quantitative and data-science teams in model validation, calibration, challenge and the communication of model implications to executives and boards.
Recruited and led data-science teams developing predictive analytics, underwriting and decision architecture for mobile savings, lending and digital-channel systems at national scale.
Conquer Risk engagements have involved senior executives, boards, regulators, data scientists and operating teams across global institutions. Competitive work has included engagements delivered with major strategy and professional-services firms.
These names reflect expert-advisory work and successful bids in which M. Antony Ewing participated; they are not presented here as a single category of direct Conquer Risk clients.
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.
Using external and internal criticism as structured information for organizational improvement.
A leadership approach to cost discipline that preserves institutional capability rather than defaulting to layoffs.
Distinguishing a one-off decision error from a structurally flawed strategy and responding accordingly.
A quantitative approach to leadership effectiveness and behavioral measurement.
Risk, resilience and strategic adaptation under fast-changing operating conditions.
Why apparently useful workplace measurement technologies can create governance and performance risks.
For executive advisory, AI and model validation, governance, data strategy, technical proposal support, clinical AI, or institutional capability building, contact M. Antony Ewing.