How Financial Institutions Are Racing to Build 'Horizontal Intelligence' Against Multi-Vector Crime

Financial criminals are increasingly operating across multiple domains simultaneously, combining fraud, cybercrime and money laundering into sophisticated attacks that exploit gaps between traditional risk management functions. According to industry research, approximately 2% of global financial crime is properly detected, a statistic that has prompted financial institutions to fundamentally rethink their approach to security and compliance.

The failure to detect coordinated threats stems largely from organizational silos and disconnected technology systems. Criminals can progress from account takeover to money laundering through mule networks within hours, moving faster than traditional defences can respond. This has catalyzed a shift toward what industry experts call "horizontal intelligence"—AI systems that analyze threats across entire user journeys rather than isolated incidents.

Background and Context

The financial crime landscape has evolved dramatically over the past decade. Whereas criminals once specialized in specific areas—such as payment fraud or money laundering—today's attackers employ multi-vector strategies that cross traditional boundaries. A single criminal operation might begin with phishing emails, progress to account takeover, and culminate in rapid fund movement through complex networks of accounts and digital wallets.

Traditional risk management systems, designed around specific compliance requirements such as anti-money laundering regulations or payment fraud prevention, struggle to identify these coordinated campaigns. Each system operates with its own data set and detection rules, creating blind spots that sophisticated criminals exploit.

Key Figures and Entities

Major financial institutions, including global banks and emerging fintech companies, are investing heavily in unified risk architectures. According to regulatory filings and industry reports, institutions such as JPMorgan Chase, HSBC, and Revolut have initiated projects to consolidate their fraud and AML detection capabilities into single platforms.

Technology providers specializing in financial crime detection have emerged as key players in this transformation. Companies including SEON, Featurespace, and Arkose Labs have developed AI systems designed to correlate signals across multiple domains—device intelligence, behavioral patterns, transaction data, and network relationships—within milliseconds of user activity.

Regulatory bodies are also responding to these developments. The Financial Action Task Force (FATF) has emphasized the need for financial institutions to adopt more holistic approaches to risk assessment, while agencies including the UK's Financial Conduct Authority have published guidance encouraging integrated fraud and financial crime frameworks.

Legal and Financial Mechanisms

Horizontal intelligence systems operate through continuous correlation of multiple data streams. Unlike traditional point solutions that analyze isolated signals—such as device fingerprinting or transaction monitoring—these platforms create comprehensive profiles of user behavior across entire interaction journeys.

Network analysis represents a critical component of this approach. By mapping relationships between accounts, devices, IP addresses, and behavioral patterns, these systems can identify suspicious clusters that would remain invisible to siloed detection methods. The technology applies graph algorithms to score relationship strengths, allowing investigators to prioritize high-risk networks rather than individual alerts.

Perhaps most significantly, modern systems incorporate feedback loops that convert investigation outcomes directly into improved detection models. When analysts confirm or dismiss alerts, these decisions automatically retrain the underlying AI algorithms, creating continuous learning cycles that adapt to emerging criminal tactics without requiring manual rule updates.

International Implications and Policy Response

The shift toward horizontal intelligence has significant implications for global regulatory frameworks. Current compliance regimes often mandate specific detection systems for different types of financial crime, inadvertently reinforcing organizational silos. Regulators are beginning to recognize how these requirements may actually hinder effective crime prevention.

In the European Union, proposals under the Anti-Money Laundering Package include provisions for more integrated data analysis across financial institutions. Similarly, the U.S. Treasury's Financial Crimes Enforcement Network has signaled increased openness to innovative approaches that combine fraud and AML detection capabilities.

Cross-border cooperation remains a persistent challenge. Criminal networks operate globally, moving funds across jurisdictions with varying regulatory requirements and data-sharing restrictions. Horizontal intelligence systems are only as effective as the data they can access, prompting renewed calls for international information-sharing agreements that respect privacy concerns while enabling effective crime detection.

Sources

This report draws on industry research including the UN Office on Drugs and Crime estimates of financial crime detection rates, regulatory guidance from the Financial Conduct Authority, and public statements from financial institutions regarding their risk management investments. Additional context was provided by technical documentation from financial crime detection platforms and academic research on network analysis applications in fraud detection.