The Challenge
Financial stability is under strain. The post-COVID era has brought a convergence of risks that existing monitoring frameworks struggle to address.
Policymakers need better tools—tools that are timely, explainable, and built to the standards the moment demands. This page outlines the challenges that motivate our work.
The Post-COVID Macro-Financial Landscape
The years since 2020 have fundamentally reshaped the macro-financial environment.
Compressed Policy Cycles
Central banks worldwide have executed some of the most aggressive rate cycles in decades. The rapid shift from near-zero rates to restrictive policy exposed vulnerabilities accumulated during the low-rate era.
Duration and Funding Stress
Banks and nonbank financial institutions faced severe interest-rate risk as asset values declined sharply. The 2023 banking turmoil demonstrated how quickly duration mismatches can become existential threats.
Nonbank Vulnerabilities
The growing role of nonbank financial intermediaries—money market funds, hedge funds, private credit—has created new channels of systemic risk that are less visible and less regulated.
Market Disorder Episodes
From Treasury market stress to crypto contagion, recent years have seen multiple episodes of market dysfunction that threatened broader stability.
What Official Assessments Show
U.S. financial authorities have documented these vulnerabilities in detail.
The Federal Reserve Financial Stability Report (April 2025) identifies ongoing risks from valuation pressures, borrowing levels, financial-sector leverage, and funding fragilities.
— Federal Reserve
The FSOC Annual Report (December 2024) emphasizes nonbank vulnerabilities, funding market stress, and the need for improved risk monitoring across the financial system.
— Financial Stability Oversight Council
The Office of Financial Research Annual Report (2024) highlights systemic-risk channels, data gaps, and the critical importance of high-frequency monitoring.
— Office of Financial Research
These official assessments make clear: the risks are known, but the tools to address them remain inadequate.
The Skills Gap
The Hybrid-Skills Problem
Effective AI/ML for financial stability requires a rare combination of capabilities. Most institutions have specialists in one or two domains—but very few have professionals who combine all five at production quality.
| Skill Domain | What's Required |
|---|---|
| Macro-Finance | Deep understanding of sovereign, banking, currency, and corporate risk dynamics |
| Machine Learning | Proficiency with modern ML methods—gradient boosting, neural networks, ensemble techniques |
| Software Engineering | Production-grade development: CI/CD, testing, version control, secure deployment |
| Interpretability | Ability to make models explainable: SHAP values, model cards, governance documentation |
| Policy Communication | Translating technical outputs into actionable insights for senior decision-makers |
In Central Banks & Regulators
- AI/ML is viewed as promising but risky to adopt
- Pilot projects stall because outputs cannot be explained
- Dependencies on external vendors create concerns
In Academia
- Research produces models that never reach production
- Papers optimize for publication, not utility
- Methods documented for researchers, not practitioners
In Private Sector
- Cutting-edge tools remain proprietary
- Commercial incentives don't align with stability objectives
- Solutions designed for revenue, not public goods
Policy Imperatives
U.S. financial regulators have been explicit about what's needed. Their priorities align directly with our work.
Early Warning Capabilities
Policymakers need indicators that update frequently enough to catch turning points, cover multiple risk dimensions, and are transparent enough to be trusted and acted upon.
Liquidity Stress Detection
Institutions need real-time funding stress indicators, tools to detect shifts in depositor behavior, and early warning of liquidity fragilities before they become runs.
Transparency Requirements
Supervisors need explainable models that can be interrogated and audited, governance artifacts that meet regulatory standards, and drift monitoring for reliability.
Rigorous Analytics
The field needs open methodologies for community review, gap-aware cross-validation for missing data, and reproducible research that others can build upon.
Why Existing Solutions Fall Short
Academic Research
Methodological innovation, peer review, theoretical rigor
Rarely production-ready; documentation for researchers, not practitioners
Private Vendors
Engineering quality, commercial support, regular updates
Proprietary "black boxes"; expensive licenses; misaligned incentives
In-House Government
Tailored to institutional needs; full control
Hybrid-skills scarcity; competing priorities; difficulty attracting talent
Existing Think Tanks
Policy expertise, credibility, independence
Limited ML/engineering capability; focus on narrative over operational tools
What's Missing
An organization that combines think-tank independence and policy expertise, production-grade engineering capabilities, state-of-the-art ML with built-in explainability, and commitment to open, public-good outputs.
This is the gap we fill.
Learn About Our Work →