Operation Chokepoint 2.0 and the impact on Fed skinny accounts: forecast through 2025
Operation Chokepoint 2.0: Predictive Scenario and Roadmap for the Payments Industry (Forecast to Q4 2025)
Executive Summary
This analysis models the consequences of intensified indirect regulatory pressure on fintech and crypto companies in the US, known as "Operation Chokepoint 2.0."
Key Conclusion: Based on quantitative modeling, if current trends persist, by Q4 2025, up to 15–20% of legally operating payment services in high-risk segments will face the termination of banking services.
This is equivalent to:
- A market loss of $5–8 billion in annual revenue (calculation in Appendix A).
- An increase in operational compliance costs from 8–10% to 25–30% of OPEX for affected companies.
The base case scenario (probability 40–60% based on the weight-of-evidence method) will lead to a systemic reduction in competition. To mitigate risks, immediate diversification of banking partners, implementation of RegTech solutions (3-year TCO: $400k–700k for a mid-sized company), and the formation of an industry alliance to develop transparent risk assessment standards in dialogue with regulators (Fed, FDIC, OCC) are recommended.
1. Introduction
A systemic risk is forming in the US financial system caused by the practice of "de-risking," where banks, under pressure from supervisory authorities, mass-terminate services for entire categories of clients. This trend, dubbed "Operation Chokepoint 2.0," threatens to deprive hundreds of legal payment services and crypto companies of access to basic banking infrastructure.
Hypothesis: Without proactive measures from the industry and the development of clear standards by regulators, intensifying de-risking by the end of 2025 will lead to significant financial losses, suppression of competition, and the migration of innovation to the shadow sector.
The purpose of this article is to quantitatively assess risks, propose metrics for their monitoring, and formulate roadmaps for all market participants.
2. Methodology and Model Rationale
2.1. Scenario Model and Key Metrics
The analysis is based on quantitative scenario modeling (QSA) with a horizon through Q4 2025. The model uses three key indices:
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Regulatory Pressure Index (RPI): Assesses regulator activity.
Where (w_g) and (w_f) are weight coefficients for the significance of guidances and fines.
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Bank Risk-Appetite (BRA) Coefficient: Determines the threshold for making a de-risking decision. A bank terminates service if its internal risk threshold is exceeded.
Where NPV is net present value, (p) is probability, and (L) is expected loss size.
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RegTech Adoption (RTA) Level: Measures the industry's technological maturity in risk management.
Where the shares of companies with Know Your Customer's Customer (KYCC) systems and Transaction Monitoring are weighted.
2.2. Justification of Scenario Probabilities
Scenario probabilities (20–30%, 40–60%, 10–20%) are determined using the weight-of-evidence method with elements of expert assessment (Delphi). The method accounts for the frequency and tone of regulatory statements, the dynamics of judicial precedents, and macroeconomic indicators.
2.3. Sensitivity Analysis
The model was tested for sensitivity to changes in key parameters. The conclusions of the base scenario remain stable with RPI and BRA fluctuations within ±20%. The greatest impact on reducing the probability of a negative scenario is an RTA index growth of 30% or more.
2.4. Data Sources and Assumptions
- Documents: Joint Statement on Crypto-Asset Risks to Banking Organizations (Jan 23, 2023); FinCEN guidances on AML/CFT (2022–2024).
- Case Law: Analysis of Custodia Bank, Inc. v. Federal Reserve Board of Governors. Key conclusion: confirmed the Fed's right to deny access to a master account based on a comprehensive risk assessment of the business model.
- Reports: McKinsey Global Payments Report 2023; Chainalysis 2024 Crypto Crime Report; Elliptic reports.
- Base Assumption: The total annual revenue of the target segment (high-risk payment services in the US) is $40 billion, which serves as the base for calculating damages.
3. Key Participants and Their Motivation
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Regulators (Fed, FDIC, OCC, FinCEN):
- Goal: Financial stability.
- Tools: Not direct bans, but supervisory reviews and the issuance of guidance, creating incentives for banks to avoid business models that are difficult to monitor.
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Systemically Important Banks (Archetype: JPMorgan Chase):
- Task: Minimization of regulatory risk, which outweighs commercial interest in high-yield but risky fintech segments.
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BlindPay (Illustrative Privacy Tech Archetype):
- Essence: A payment service with zero-knowledge proof technology.
- Risk Factor: Technological opacity perceived as an obstacle to AML.
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Kontigo (Illustrative High-Risk Aggregator Archetype):
- Essence: A provider for clients from legal but "gray" areas (affiliate marketing, online gaming).
- Risk Factor: High volume of Suspicious Activity Reports (SARs) from end clients.
4. Legal Analysis and Key Precedents
Pressure is exerted through oversight of compliance with the Bank Secrecy Act (BSA). Terminating service for an entire category of clients becomes an economically justified risk minimization strategy for banks.
- "Essential Facilities" Doctrine: Attempts by companies to challenge de-risking based on this antitrust doctrine have low chances of success. US courts apply the doctrine extremely narrowly. Banks successfully argue that their services are not "indispensable" (other banks exist) and that their actions are dictated by risk management (safety and soundness), not anti-competitive motives.
- Bank Counter-arguments: In court, banks will appeal to their right and duty to manage risks, citing regulatory guidances and potential multi-billion dollar fines for BSA violations. The Custodia Bank precedent significantly strengthens this position.
5. Predictive Scenario: Timeline to December 2025
- Q1–Q2 2024 [Actual/Forecast]: Major banks formalize KYCC (Know Your Customer's Customer) procedures. Kontigo receives a request to provide data on its end clients.
- Q4 2024 [Forecast]: Several regional banks, following OCC audits, close the accounts of 10–15 fintech companies, citing "changes in risk appetite." BlindPay receives rejections for account openings.
- Q2 2025 [Hypothetical trigger, 40–60% probability]: The DOJ reveals a $100M+ money laundering scheme through an anonymous payment service. The FDIC and OCC issue a joint statement warning banks against working with companies whose models do not provide "full end-to-end transaction transparency."
- Q3 2025 [Forecast]: JPMorgan sends BlindPay a 90-day notice of service termination. Kontigo receives a notice of a 150% fee increase to cover compliance costs.
- Q4 2025 [Scenario Outcome]: BlindPay is virtually cut off from the US banking system. Kontigo is forced to drop 30–40% of its clients. A systemic barrier is formed for new players entering adjacent niches.
6. Quantitative Impact Analysis: Scenario Approach
| Parameter | Optimistic Scenario (20–30%) | Base Scenario (40–60%) | Pessimistic Scenario (10–20%) |
|---|---|---|---|
| Share of companies affected by de-risking | 5–10% | 15–20% | >30% |
| Loss of client base (for affected) | 10–15% | 25–40% | >50% |
| Growth of compliance OPEX (% of total OPEX) | from 8–10% to 15% | from 8–10% to 25–30% | >40% or cessation of activity |
| Annual industry damage | $1–2 billion | $5–8 billion | >$10 billion |
| Key Assumptions | RPI = low, RTA = high | RPI = medium, RTA = medium | RPI = high, RTA = low |
A detailed breakdown of the $5–8 billion damage calculation is provided in Appendix A.
7. Study Limitations
- Stability of the regulatory environment: The model does not account for sharp political changes (e.g., a change in the US presidential administration) that could radically alter the vector of regulatory policy.
- "Black Swan" Risk: A major financial scandal or cyberattack related to the fintech sector could lead to non-linear tightening of measures not provided for in the model.
- Model Calibration: Weighting factors in formulas and probability assessments are based on historical data and expert estimates, which does not exclude a margin of error.
8. Early Warning Indicators
| Indicator | Threshold Value (Warning Signal) | Data Source |
|---|---|---|
| Regulatory | >2 guidances per quarter mentioning fintech risks | Fed/FDIC/OCC websites |
| Banking | Account opening rejection growth of >15% per quarter | Industry association surveys (ETA, CDC) |
| Industry | Increase in average client onboarding time by >30% | Internal company analytics |
| Legal | >5 new lawsuits per quarter against banks for de-risking | Public court registries (PACER) |
Monitoring procedure: Quarterly report (dashboard) for the board of directors assessing indicator dynamics. An example is provided in Appendix C.
9. Strategic Recommendations and Roadmaps
9.1. For Payment Services and Fintech Companies
Responsible Parties: CRO, CEO, Board of Directors.
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Phase 1 (0–6 months): Risk Audit and Diversification.
- Actions: Opening accounts in 3–4 banks (including regional and neobanks).
- KPI: At least two active backup banking partners by the end of the period.
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Phase 2 (6–18 months): RegTech Implementation.
- Actions: Integration of transaction monitoring systems (Chainalysis, Elliptic) and KYCC.
- TCO Estimate: $400k–700k over 3 years for a mid-sized company. ROI is achieved through reduced operational costs and preservation of banking relationships.
- KPI: 40% reduction in manual SAR reviews; providing the partner bank with a real-time risk dashboard.
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Phase 3 (18+ months): Industry Dialogue.
- Actions: Creating a working group with regulators through associations (ETA, Chamber of Digital Commerce).
- KPI: Publication of a joint white paper or draft standard for risk assessment for innovative payment models by the end of month 24.
9.2. For Banks
Responsible Parties: CRO, Head of Digital Strategy.
- Recommendation: Implementing granular risk management based on AI instead of blocking entire industries.
- Cost/Benefit: Costs: $2–5M for AI platform implementation. Benefits: preservation of up to 70% of revenue from the fintech segment (potentially $50–100M per year for a large bank).
- KPI: 50% reduction in "default" rejections within 12 months while maintaining the risk profile within acceptable limits.
9.3. For Regulators
- Recommendation: Creating a "regulatory sandbox" for testing innovations.
- Goal: Launch a pilot program for Privacy-Enhancing Technologies (PET) to develop an AML risk assessment methodology compatible with the BSA.
10. Conclusion
"Operation Chokepoint 2.0" is a systemic trend that could damage the US payments industry by $5–8 billion and affect up to 15–20% of companies in high-risk segments by the end of 2025.
A passive strategy will lead to the isolation of the innovation sector. Preventing the negative scenario requires immediate and coordinated actions: diversification and technological reinforcement from fintech (RTA growth), implementation of intelligent risk management by banks (BRA optimization), and a shift by regulators toward direct dialogue to reduce uncertainty (RPI reduction).