Banks and wealth managers face serious challenges and difficult trade-offs when working with high-risk clients, especially as regulatory scrutiny becomes more intense and enforcement actions increase. The rewards—significant profits, global expansion, and a competitive advantage—are genuine, but so are the risks: heavy fines, damage to reputation, and operational disruptions. Recent enforcement actions against HSBC, Julius Baer, and Morgan Stanley show why robust compliance, active surveillance, and proactive AI must be key for both monitoring clients and overseeing staff.
The Risks and Rewards of High-Risk Clients
Banks are drawn to high-risk clients—politically exposed persons (PEPs), cross-border elites, sanctioned entities, and other complex global profiles—because of their transaction volume and fee potential. Yet these relationships bring disproportionate exposure to financial crime, sanctions violations, and regulatory risk, often requiring enhanced due diligence and costly ongoing monitoring.
- HSBC was fined $1.9 billion in 2012 for massive AML failures, including facilitating laundering for drug cartels and breaching sanctions. Subsequent probes and fines followed in 2021 and 2024, as weaknesses in its AML transaction-monitoring systems persisted despite overhaul and increased regulatory pressure.
- Julius Baer incurred a $5 million order from FINMA in 2025 for grave AML lapses, including failing to detect suspicious activity among high-risk clients and mismanagement of due diligence records—after earlier penalties in multiple jurisdictions.
- Morgan Stanley has recently faced investigation by FINRA and additional federal regulators, who found its client onboarding, risk ranking, and AML controls insufficient for flagging complex risks among international wealth clients and associates, with orders to revamp policies and pay penalties for failing to monitor and supervise staff misconduct.
These examples make clear that wealth managers who prioritize profit often expose themselves to extensive, long-term compliance risk and regulatory action.
The New Compliance Imperative: Agentic AI
Agentic AI—advanced generative or autonomous artificial intelligence—marks a transformational shift from rule-based systems to dynamic, self-improving networks that proactively identify, adapt to, and mitigate risk.
How Agentic AI Builds Risk Resilience
- Holistic Risk Profiling: AI aggregates structured and unstructured data (transaction histories, global databases, adverse media), enabling real-time, context-rich client profiles and revealing risks invisible to static reviews.
- Continuous Monitoring and Rapid Stratification: AI systems update risk assessments instantly as new information emerges, continuously flagging client and associate activities for additional review or escalation.
- Enhanced Due Diligence and Pattern Detection: AI automates analysis of millions of data points—identifying hidden associations, exposure to sanctioned entities, and complex webs of relationships between clients and staff.
- Proactive Stress Testing: By generating synthetic transaction scenarios, agentic AI can stress-test controls, simulate vulnerabilities, and find weaknesses before real-world exploitation occurs.
- False Positive Reduction: Machine learning allows systems to learn from past alerts and compliance decisions—filtering noise and focusing teams on truly suspicious activity.
Incorporating AI Into a Comprehensive Supervision Program
For agentic AI to deliver its full promise, it must be embedded within a broad regime that integrates:
- Transaction Monitoring: AI systems scan client accounts and relationships as well as staff-initiated transactions, identifying anomalies, incompatible behaviors, and “red flags” indicating possible collusion or enabling of client crime.
- Associate Oversight: Monitor staff communications, account access, and approval patterns against risk models—flagging unusual staff-client interactions, changes in access privileges, and gaps in internal controls.
- Audit Trails and Transparency: Every AI-driven risk decision must be logged, traceable, and available for regulator review, allowing banks to demonstrate why and how decisions were made.
- Culture and Governance: AI should support, not replace, human oversight, with leaders encouraging diligence, accountability, and a ‘speak up” culture at all levels.
Looking Ahead
With high-risk clients and international partners under a global spotlight, banks and wealth management firms must go beyond legacy compliance and recognize that agentic AI is not just an optional innovation—it’s a regulatory and operational necessity. Only by integrating powerful technology with cultural change and steadfast governance can institutions hope to manage the risks and seize the rewards of complex client relationships, while steering clear of the regulatory and reputational dangers vividly highlighted by HSBC, Julius Baer, and Morgan Stanley.
Our RiskPulse platform provides banks and wealth managers with a comprehensive set of tools specifically designed for managing high-risk client relationships. Using advanced AI-driven agents that think and act like investigators, RiskPulse automates and streamlines even the most complex aspects of risk assessments, KYC, and AML investigations. The platform pulls data from various sources, constructs structured case narratives, and enables real-time transaction analysis, helping teams quickly detect suspicious activities and hidden patterns in both client and associate behaviors. RiskPulse also automates due diligence, policy mapping, and sanction reviews, offering clear, standardized outputs for regulators and compliance leaders. Its flexible design integrates smoothly with legacy systems or modern cloud setups, offers customizable workflows, and maintains enterprise-grade security for sensitive data. For organizations managing high transaction volumes and complex global risks, RiskPulse empowers compliance teams to expand investigative capacity, improve enforcement consistency, and proactively address vulnerabilities before they can be exploited.
