🛟 Banking boards enter the AI-driven AML battlefield unprepared

Rewrite the rules. Outmaneuver competitors. Stay two steps ahead of regulators.

From New York to Sydney, AI Check In delivers sharp, globally relevant intelligence on AI governance, financial risk, and capital automation.

Already briefing strategy, risk, and compliance leaders at the world’s most influential banks, including JPMorgan, Citi, and Bank of America.

What to expect this edition:

• 🛟 Need to Know: Boards Face a New AML Reckoning

• 🥷 Deep Dive: Wells vs. JPM - Reactive Compliance or Strategic Deterrence?

• ⚔️ Vendor Spotlight: AI solutions for AML

• 🔭 Trends to Watch: Where AML Strategy Breaks or Wins

The AI race is on. Let the games begin.

🛟 Need to Know: Boards Face a New AML Reckoning

U.S. anti-money laundering (AML) enforcement is shifting rapidly from broad procedural checks to targeted crackdowns on governance failures. In 2024 alone, banks faced $4.2 billion in AML fines, with average penalties up 27%, signaling regulators' impatience with surface-level compliance.

Building on the AML Act of 2020, the new 2024 FinCEN proposals require institutions to conduct comprehensive risk assessments, embed FinCEN’s national AML priorities and, crucially, mandate direct board-level approval and oversight of AML programs. This marks a clear pivot from static, rules-based frameworks to dynamic, risk-driven approaches.

A 2025 ABA survey shows 91% of executives are eager to deploy AI for fraud and AML detection, but enthusiasm without strong governance is a liability. Boards must enforce SR 11-7-aligned model validation, demand real-time override protocols, and ensure strict data lineage and privacy compliance under GLBA and CCPA.

🥊 Your Move:

• Treat AI-driven AML as a board-level strategic asset, not a compliance cost center, to align with evolving FinCEN expectations and national priorities.

• Enforce continuous validation and override governance, to fully align with SR 11-7 and evolving BSA expectations by integrating dynamic risk assessments.

• Leverage AML governance as a competitive differentiator, strengthening capital position and reducing reputational exposure.

🥷 Deep Dive: Wells vs. JPM - Reactive Compliance or Strategic Deterrence?

Wells Fargo: Remediation and Risk Rebuilding

In September 2024, the Office of the Comptroller of the Currency (OCC) entered into a formal agreement with Wells Fargo Bank, N.A., identifying serious deficiencies in its financial crimes risk management and AML controls.

These included failures in suspicious activity reporting, customer due diligence, and beneficial ownership verification. Wells Fargo was required to submit a comprehensive action plan within 120 days and establish a Compliance Committee with independent directors overseeing remediation.

While Wells Fargo has reaffirmed its commitment to strengthening its AML and sanctions risk management practices, it has not publicly disclosed major AI-driven advancements. The focus remains on rebuilding foundational governance and satisfying regulators. While this is a necessary step, it reflects a reactive, compliance-centric posture rather than a strategic deterrence model.

JPMorgan Chase: AI as a Strategic Moat

In sharp contrast, JPMorgan Chase has embedded AI into its AML and broader risk frameworks as a competitive weapon. The bank’s behavior-centric AI systems analyze transaction patterns, device telemetry, and behavioral biometrics to detect hidden laundering networks beyond legacy rules-based detection.

Operational results are striking: JPMorgan reports reductions in false positives of up to 95%, freeing compliance teams to focus on high-risk cases. The use of synthetic data enables continuous stress testing and privacy-compliant model improvements, aligning with the AML Act of 2020 and FinCEN’s 2024 push for dynamic, risk-based frameworks with direct board accountability.

JPMorgan’s integration of AML intelligence into enterprise risk dashboards informs credit, cyber, and capital allocation strategies. This unified client risk view supports strategic decisions far beyond compliance, reinforcing the bank’s deterrence posture. JPMorgan’s governance includes continuous model drift monitoring, override thresholds, and direct board-level reporting — operationalizing AI as a strategic moat rather than a cost center.

As CEO Jamie Dimon noted, "We took AI and data out of technology. It's too important. We put AI at that management table." This statement underscores a cultural commitment to AI-driven governance that many institutions still lack.

Lessons from HSBC and Bradesco

HSBC’s AI-first AML transformation demonstrates the power of bold strategic commitment. The bank achieved 2–4x more suspicious activity detection while reducing alert volume by over 60% and false positives by 20%, materially improving analyst productivity and customer experience. Their “burn the ships” approach, eliminating parallel rule-based systems, underscores their willingness to leap ahead rather than iterate cautiously.

Banco Bradesco, by contrast, took a phased approach with initial parallel runs to build internal confidence. Both banks emphasized that data preparation is the critical barrier: fragmented and poor-quality data severely limits AI effectiveness. Addressing these issues was essential before realizing operational gains.

Data Quality as a Strategic Foundation

AI’s impact on AML is fundamentally tied to data integrity. Major banks like HSBC and JPMorgan reported major gains from AI-driven data cleansing, using machine learning-based deduplication, NLP for unstructured data, and anomaly detection. Strong KYC foundations are equally critical. Fragmented onboarding data undermines every downstream AML insight, regardless of AI sophistication.

Future trends include federated learning (to improve models collaboratively without sharing raw data) and explainable AI to satisfy regulators' transparency demands. These shifts make data quality a board-level strategic asset rather than a back-office concern.

Governance and Cultural Divergence

The divergence between Wells Fargo’s remediation focus and JPMorgan’s AI-driven deterrence underscores that success is not just about technology. JPMorgan’s leadership signals a cultural readiness to embed AI as a strategic asset, reinforced by governance structures that actively oversee cross-silo risk signals and model performance. This shift aligns with FinCEN’s 2024 emphasis on board accountability and continuous, dynamic risk frameworks.

🥊 Your Move:

• Embed AML AI as a board-supervised strategic asset, aligning with FinCEN’s 2024 expectations and evolving national priorities.

• Invest in unified data foundations, including KYC, to strengthen cross-silo intelligence and future-proof model resilience.

• Benchmark against forward-leaning leaders, closing gaps between AI enthusiasm and enterprise-scale execution readiness.

⚔️ Vendor Spotlight: AI solutions for AML

TL;DR: No vendor is perfect. Each choice is a strategic bet: corridor speed, unified intelligence, or full-spectrum control. Boards should match vendor selection to strategic risk appetite rather than brand comfort.