The fight against financial and economic crime is under increasing pressure worldwide due to rapid digitalisation, cross-border operational structures and an ever-evolving risk landscape. In this context, there is a growing need for an enforcement model that no longer functions in a fragmented and reactive manner, but is instead characterised by an integrated deployment of expertise, technology and governance structures. Traditional approaches, often based on organisational and legal compartmentalisation, have proven insufficiently equipped to identify complex threats in a timely manner and to mitigate them effectively. This creates a clear need for a refined framework in which information sharing, risk-driven interventions and international harmonisation take a central role.
This development calls for a recalibration of both the strategic and operational enforcement paradigm. The formation of multidisciplinary teams, the institutionalisation of public-private cooperation and the application of advanced data-processing techniques are essential components of this shift. At the same time, the intensification of such measures requires a high degree of legal precision, with particular attention to non-compliance with the GDPR, proportionality and procedural safeguards. This contribution explores the foundations of an integrated and future-proof enforcement architecture and elaborates the key building blocks in detail across the following sections.
Transition from Silo-Based Enforcement to an Integrated, Multidisciplinary Approach
An integrated, multidisciplinary enforcement approach constitutes a crucial starting point for the effective fight against modern forms of financial and economic crime. Traditional practice, in which regulators, investigative agencies and private entities each operate exclusively within their own discipline, often leads to fragmented information, suboptimal interventions and limited recognition of underlying criminal patterns. An integrated model enables authorities to combine expertise in financial analysis, legal assessment, investigative operations and technological detection, thereby allowing complex structures—such as layered money-laundering schemes and cross-border fraud—to be uncovered more rapidly. This model not only enhances effectiveness but also strengthens the consistency and legitimacy of enforcement decisions.
The implementation of a multidisciplinary structure requires robust governance arrangements to ensure that differing mandates, responsibilities and safeguards are properly aligned. Within such structures, it is essential that information sharing is regulated with precision, both from a legal perspective and from the standpoint of organisational risk management. An integrated approach therefore requires detailed protocols for data-linkage, joint analysis and collective decision-making, with systematic prevention of non-compliance with the GDPR. This includes embedding proportionality assessments, storage limitations and purpose-limitation requirements within operational cooperation.
Furthermore, a multidisciplinary approach enhances the adaptability of enforcement bodies in an environment of accelerating digitalisation. Through the combined deployment of knowledge relating to fintech structures, cyber modus operandi and international market mechanisms, authorities can respond more effectively to emerging threats. An integrated model also facilitates the continuous updating and exchange of expertise, allowing enforcement organisations to operate not only reactively but also proactively within a dynamic criminological ecosystem.
Intensification of Public-Private Cooperation for Early-Stage Detection
Public-private cooperation is a critical pillar in the early detection of financial crime, as private institutions—such as banks, insurers and payment service providers—generate and monitor a substantial share of relevant transactional data. Intensifying cooperation enables a more comprehensive view of unusual activities, as signals that may not appear suspicious to individual parties can be identified through joint analysis. These partnerships also create a platform for sharing best practices, sector-specific risk indicators and emerging modus operandi, thereby significantly enhancing detection capability.
Institutionalising such cooperation requires carefully designed legal frameworks. Governance models must ensure that the exchange of operational information occurs within the boundaries of financial-supervision law and data-protection regulation, preventing non-compliance with the GDPR by requiring strict adherence to data-minimisation principles and transparency requirements. To this end, the use of secure data rooms, controlled analytical environments and predefined data categories plays a central role. These mechanisms ensure that cooperative analyses can be conducted without unnecessary or unauthorised data flows.
Additionally, public-private cooperation strengthens the quality of risk assessment by combining behavioural indicators, market data and historical incident information. This allows emerging risks to be detected and prioritised at an earlier stage. By aligning insights, feedback processes and joint evaluations on a structural basis, a continuously improving detection model is formed—one that is both efficient and proportionate. This leads to a more uniform and timely approach to risk across the financial sector.
Systematic Data Sharing Between Regulators, FIUs and Market Participants
Systematic and legally safeguarded data sharing is a cornerstone of contemporary enforcement architecture. Regulators, Financial Intelligence Units (FIUs) and market participants each possess unique datasets which, when combined, can generate critical insights for risk detection, network analysis and the tracing of financial flows. Harmonising data streams and enabling data-linkage within a controlled GDPR-compliant framework creates a far more comprehensive understanding of risks and potential criminal structures.
Developing a sustainable data-sharing model requires not only technological solutions but also a streamlined regulatory framework. Legal safeguards must ensure that data-linkage occurs solely for well-defined purposes, that access is restricted based on strict necessity and that audit mechanisms are in place to prevent structural non-compliance with the GDPR. The implementation of synthetic-data solutions, tokenisation and pseudonymisation can support this objective by preserving analytical value without requiring direct identification of individuals.
Systematic data sharing also enables the detection of economic-crime patterns in real time. The use of machine-learning models, advanced linkage techniques and risk-scoring mechanisms allows abnormalities to be identified more quickly, enabling authorities to intervene more appropriately and more promptly. This not only enhances the effectiveness of supervision and enforcement but also ensures more proportionate use of resources by focusing interventions on the most significant risks and transactional flows.
Risk-Based Prioritisation Using Advanced Analytics
A risk-based approach is the backbone of an efficient enforcement system, allowing resources to be allocated to areas where threats or impacts are greatest. The use of advanced analytics—ranging from pattern recognition and anomaly detection to network modelling and probabilistic risk scoring—enables authorities to uncover underlying structures of financial crime at an early stage. These methods can detect signals that traditional compliance processes fail to identify due to their scale, complexity or layered nature.
However, the use of such technologies requires careful legal and ethical grounding. Because analytical systems process large volumes of data, strict adherence to the GDPR is essential, with particular attention to purpose limitation, lawfulness and the transparency of algorithmic decision-making. Robust controls are also required to prevent datasets from containing inadvertent biases that could distort analytical outcomes. A thoroughly designed governance structure—including periodic model audits, human-oversight mechanisms and extensive documentation requirements—is indispensable in this respect.
Combining risk-based prioritisation with advanced analytics results in an adaptive supervisory model aligned with the dynamics of modern criminality. Analytical outcomes not only support the prioritisation of investigations, but also refine the allocation of resources, monitoring of sector-specific trends and the design of joint enforcement programmes. This enables more targeted intervention against complex risks without imposing disproportionate compliance burdens on entities with a low-risk profile.
Harmonisation of Definitions and Procedures for Cross-Border Cases
In a global financial environment, the harmonisation of definitions, procedures and enforcement methodologies is indispensable for effectively combating cross-border criminality. As countries often employ divergent legal concepts—such as different classifications of money-laundering indicators, fraud typologies, reporting obligations and evidentiary standards—criminal actors can exploit these inconsistencies to evade oversight. Harmonisation promotes predictability, coherence and operational viability of international enforcement actions, facilitating cooperation between regulators, FIUs and judicial authorities.
The harmonisation of procedures requires structural alignment on matters such as data sharing, investigative powers, licensing requirements and sanctioning mechanisms. Within this process, it is critical that safeguards relating to privacy, legal certainty and proportionality are brought to comparable levels across jurisdictions. Non-compliance with the GDPR is a central concern, given that international data sharing often involves transfers to third countries. This necessitates binding data-protection arrangements, contractual safeguards and oversight mechanisms to ensure that data processing remains consistent with European standards.
Moreover, harmonisation of definitions and procedures creates opportunities for coordinated international actions, including simultaneous investigations, joint audit programmes and shared intelligence platforms. This approach not only enhances enforcement effectiveness but also strengthens deterrence by reducing the ability of criminal actors to exploit regulatory divergences. Harmonisation thus forms a central component of a future-proof strategy against cross-border financial and economic crime.
Increased Focus on GDPR Non-Compliance in Data-Driven Investigations
The intensification of data-driven investigations within the domain of financial and economic crime inevitably entails the large-scale processing of personal data. This development requires a significantly strengthened focus on preventing non-compliance with the GDPR, as such investigations typically involve extensive datasets, advanced analytical techniques and international data flows. Within this context, it is essential that the legal foundations of each investigation be anchored in strict purpose limitation, proportionality and necessity. These safeguards form the basis for an investigative methodology that is both effective and legally sustainable, substantially reducing the risk of systematic breaches of data-protection regulation. This means that, even in the preparatory phase of an investigation, attention must be devoted to data classification, data cleansing and the identification of lawful grounds for processing.
The deployment of analytics and automated detection systems further demands careful technical and legal calibration. Algorithmic decision-making may result in the processing of datasets that exceed what is strictly necessary, thereby elevating the risk of non-compliance with the GDPR where adequate safeguards are not embedded. Practical tools that support compliant investigative practice include pseudonymisation, layered access-control structures, audit trails and explicit review checkpoints conducted by independent data-protection specialists. By structurally integrating these safeguards into governance models, an investigative framework is created that combines technological innovation with rigorous legal oversight.
International cooperation also presents a critical point of attention in the context of GDPR non-compliance. Many financial-crime investigations require cross-border data exchange among supervisory authorities, FIUs and private-sector entities. Where personal data is transferred to third countries without appropriate safeguards, material compliance risks arise. Accordingly, the legal design of cooperation mechanisms must include binding data-protection arrangements, periodic compliance audits and risk-mitigation measures relating to onward transfers. These elements ensure a solid foundation for data-driven investigations that operate both effectively and in full alignment with applicable European standards.
Strengthening Asset-Recovery Mechanisms and Financial Tracing
The fight against economic crime only achieves real impact when unlawfully obtained assets can be successfully identified, secured and returned to society. This requires substantial reinforcement of asset-recovery mechanisms in combination with advanced methods of financial tracing. Modern criminal structures increasingly rely on decentralised financial flows, complex offshore arrangements and hybrid entities, making tracing exceedingly difficult without integrated tools and multidisciplinary expertise. By intensifying cooperation between financial regulators, investigative bodies and private institutions, a more complete picture of the origin and destination of funds can be established, significantly enhancing the effectiveness of recovery efforts.
Reinforcing asset-recovery mechanisms also demands a robust legal foundation. Jurisdictions often apply divergent rules regarding seizure, evidentiary thresholds and asset-ownership determination, complicating cross-border recovery efforts. A uniform and predictable framework contributes to the more efficient execution of tracing activities, while simultaneously requiring careful incorporation of legal safeguards to prevent disproportionate or unlawful interference. In this respect, GDPR non-compliance constitutes a relevant concern, as financial tracing frequently entails the processing of personal data originating from multiple sources. It is therefore crucial to apply privacy-by-design principles, including controlled access, data-minimisation practices and clearly articulated legal grounds for processing.
Strengthening asset-recovery efforts further depends on investment in technological detection instruments. Advanced blockchain-analysis tools, network-visualisation techniques, financial-data-linking capabilities and artificial-intelligence systems can reveal patterns invisible to traditional investigative means. However, the effectiveness of such tools hinges on close coordination between legal and operational disciplines. This enables the creation of an integrated model in which tracing activities proceed more efficiently, withstand legal scrutiny and align closely with international best practices.
Internationalisation of Sanctions and Enforcement Actions (Joint Actions)
The internationalisation of sanctions and the organisation of coordinated enforcement actions constitute a necessary response to the global interconnectedness of financial crime. Criminal networks rarely operate within a single jurisdiction and actively exploit disparities in legislative frameworks, enforcement intensity and supervisory capacity. By harmonising international sanctions regimes and conducting joint enforcement actions, a more level playing field can be achieved, substantially increasing the effectiveness of measures. This enhances deterrence and restricts the ability of criminals to select jurisdictions based on regulatory weaknesses.
However, the organisation of joint actions requires extensive legal and operational alignment. Variations in investigative powers, evidentiary standards and information-exchange protocols can limit the effectiveness of such initiatives. By developing pre-defined procedures that establish clear responsibilities, data flows and decision-making mechanisms, cooperation can take place efficiently and in a compliant manner. GDPR non-compliance represents a significant consideration in this context, particularly where joint actions involve transfers of personal data to third countries. Certified transfer mechanisms, binding agreements and transparent logging procedures are therefore essential to ensure ongoing compliance.
The internationalisation of sanctions also strengthens the capacity to combat economic crime effectively by expanding the strategic reach of enforcement measures. Joint analyses, shared intelligence structures and simultaneous operations enable authorities to dismantle criminal networks that would otherwise remain beyond the reach of individual jurisdictions. When combined with precise proportionality assessments and continuous evaluation of measures, this approach results in an enforcement framework that is both operationally robust and legally well-grounded.
Integration of ESG Fraud into the Broader Economic-Crime Domain
ESG-related fraud is rapidly emerging as a distinct risk category within the broader field of economic crime. Growing societal and regulatory focus on sustainability reporting, climate-related risks and social-governance norms creates new incentives for deception, data manipulation and misrepresentation of sustainability claims. Integrating ESG fraud into existing detection and enforcement structures is therefore of central importance, requiring the development of objective risk frameworks, sector-specific analytical methodologies and clear definitions of fraudulent behaviour. This encourages a coherent and unified system in which ESG risks are treated not as a peripheral niche, but as a fully-fledged component of economic crime.
Addressing ESG fraud additionally demands a strong interplay between legal, technical-analytical and sector-specific expertise. ESG claims often rely on complex qualitative and quantitative data streams, meaning that manipulation cannot be detected without thorough understanding of reporting procedures, sustainability indicators and audit methodologies. This context gives rise to heightened GDPR risks, as ESG analyses may indirectly involve personal data, for instance where sustainability-related information can be traced back to individual conduct within supply chains. It is therefore essential that compliance mechanisms incorporate data-minimisation requirements, transparency safeguards and protections against unauthorised dataset linkage.
Furthermore, integrating ESG fraud into the broader crime domain constitutes an important step towards international harmonisation of sustainability standards. Through cooperation among financial regulators, market participants and international organisations, shared definitions, detection indicators and enforcement strategies can be developed that are applicable across borders. This not only enhances supervisory effectiveness but also contributes to a more level regulatory environment, preventing the exploitation of discrepancies in ESG frameworks.
Proportionality and Legal Safeguards in the Context of Intensive Monitoring
The use of intensive monitoring instruments — including advanced data processing, transactional surveillance and AI-enabled detection systems — carries inherent risks for proportionality and legal protection. Although these tools are vital to the effective fight against financial crime, they must not lead to unnecessary or unjustified intrusions on privacy or other fundamental rights. A carefully structured proportionality-assessment framework is therefore essential, requiring continuous evaluation of whether the tools employed are genuinely proportionate to the intended objective and whether less intrusive alternatives are available. Such assessments must be performed not only during the design phase but also periodically throughout operational deployment.
Legal protection also plays a central role in this framework. Intensive monitoring may result in automated alerts, risk classifications and interventions that can have significant implications for individuals or organisations. It is therefore crucial that transparency mechanisms, independent reviews and complaint procedures are firmly embedded within the governance structure of monitoring systems. GDPR non-compliance again constitutes a pertinent risk, as monitoring frequently involves large-scale data processing and profiling. By ensuring strict adherence to purpose limitation, data minimisation and retention-control measures, legal protection can be effectively maintained.
A proportional and legally sound monitoring framework also contributes to preserving public trust in enforcement processes. When transparency, diligence and rule-of-law guarantees are visibly and consistently upheld, broader legitimacy is created for the intensive interventions needed to combat sophisticated forms of economic crime. The enforcement ecosystem thus remains not only effective but also sustainable and socially supported.
