CUBE Launches Certified Connector for Service Now

CUBE has launched a certified connector for ServiceNow Integrated Risk Management (IRM), making its regulatory intelligence and change management capabilities available within enterprise risk workflows. The integration is aimed at helping firms, particularly in highly regulated sectors such as financial services, connect regulatory updates and obligations more directly to compliance, risk and legal processes.

The integration links real-time regulatory updates and obligations from CUBE’s RegPlatform to ServiceNow IRM. CUBE is positioning that as a way to help organisations operationalise regulatory change more effectively within existing risk and compliance workflows, rather than relying on separate manual processes.

That positioning comes through clearly in the executive commentary. Ben Richmond, Founder & CEO of CUBE, said: “As regulatory complexity continues to grow, organisations need intelligence that works inside their existing platforms. Our partnership with ServiceNow extends CUBE’s mission to make regulatory change automated, actionable and embedded across the enterprise. Together with ServiceNow, we’re enabling firms to move faster, act earlier and manage compliance and risk at scale with far greater confidence.”

ServiceNow frames the integration in similar terms, with an emphasis on automation and workflow orchestration. “Regulatory change shouldn’t require manual tracking and endless coordination – it needs intelligent automation,” said Vasant Balasubramanian, Group Vice President and General Manager of Risk, ServiceNow. “By embedding CUBE’s intelligence into ServiceNow IRM, our AI agents can assess regulatory impact, trigger risk workflows, and orchestrate responses across the business. This gives risk and compliance leaders the ability to stay ahead of regulations and turn compliance obligations into competitive advantage.”

The broader significance is that regulatory change management is being tied more closely to enterprise risk operations. Instead of sitting outside core workflows, regulatory intelligence is being fed into the systems firms use to assess impact, assign actions and manage response processes.

CUBE says the partnership is backed by executive leadership and supported by a coordinated product and go-to-market strategy. It also expects to integrate select CUBE AI regulatory agents into the ServiceNow AI Control Tower, extending automation for shared customers using CUBE RegPlatform alongside ServiceNow IRM.

Quest Launch Bridges Identity Threat Detection and Recovery for Microsoft Operational Resilience

Quest Software has introduced a new Security Management Platform that brings together identity threat detection and response, recovery, and migration for Microsoft environments. The launch reflects a broader shift in identity security: firms are looking beyond detection alone and towards platforms that can also support resilience, recovery, and lower-risk modernization.

The company is positioning the platform around a practical problem many organizations face as identity becomes a more exposed control point. Hybrid Microsoft estates, the expansion of non-human identities, and wider AI adoption are increasing the volume and complexity of identity-related risk. In that context, Quest is combining threat management with secure migration and disaster recovery, aiming to reduce the operational gaps that can appear during periods of change such as cloud transitions, mergers, or infrastructure modernization.

Identity remains a growing attack surface, but recovery readiness is often less mature than detection. Quest points to its own research showing that more than 75 percent of global organizations do not test identity recovery often enough. It also cites the rapid growth of non-human identities, which it says now outnumber human identities by an estimated 82:1 ratio. Against that backdrop, the new platform is designed to give organizations broader visibility across identities, faster incident response, and a more structured way to recover critical services after an attack.

“AI fundamentally threatens the identity landscape at a level never before seen, and one thing is clear – identity security must include rapid recovery, not just detection and response,” said Michael Laudon, Chief Product and Technology Officer at Quest Software. “The Quest Security Management Platform delivers solutions that address how identity risk actually occurs within organizations – across daily operations, active attacks, and during high-risk moments of change such as AI adoption amid migration and modernization. By unifying our industry-leading solutions into one modern platform, customers no longer need multiple solutions to protect their most important assets.”

Two new capabilities sit at the centre of the release. Quest Identity Defence is described as an AI-powered security layer for hybrid Active Directory and Entra ID environments. It is intended to identify identity risk continuously, block unauthorized changes to critical Tier 0 assets, and improve visibility across both human and non-human identities. The emphasis is on making investigation and remediation more manageable in complex hybrid estates.

Quest Identity Recovery extends that focus into resilience. The offering is positioned as a hybrid Active Directory and Entra ID recovery solution that can restore identity services more quickly after a ransomware incident. A new Standby Active Directory Forest provisioning feature is intended to automate the creation of recovery environments, strengthening disaster recovery preparedness rather than treating recovery as a separate downstream process.

The platform also incorporates On Demand Migration, Quest’s Microsoft 365 certified tenant-to-tenant migration technology, covering Exchange, OneDrive, SharePoint, Teams, as well as Active Directory and Entra ID. That inclusion is notable because migration is often one of the periods when identity controls are under the most strain. Folding migration into the same platform as detection and recovery suggests Quest is framing modernization itself as a security and resilience issue, not just an infrastructure project.

Quest says the overall platform aligns with the National Institute of Standards and Technology Cybersecurity Framework 2.0 and Gartner recommendations. Structurally, it groups the offering into two main solution areas: Quest Secure Migration, which focuses on modernization with a security-first approach, and Quest Identity Security and Resilience, which is positioned as a full Identity Threat Detection and Response capability spanning prevention, detection, response, and recovery.

Regnology Extends Ascend Platform with Agentic AI to Operationalise Continuous Regulatory Intelligence

Regnology has expanded its Ascend platform with an agentic AI layer and deeper integration of its Regnology Supervisory Hub (RSH), positioning the platform as a unified environment for both regulatory reporting and supervisory oversight. The update builds on Ascend’s initial rollout in late 2025, which focused on data governance, automation and workflow orchestration across regulatory reporting processes.

The latest iteration shifts the emphasis from workflow automation to adaptive, intelligence-led operations. By embedding AI agents directly into the platform, Regnology is aiming to move reporting processes away from periodic, rules-based execution towards continuous monitoring and decision support. These agents are designed to manage workflows, analyse regulatory data and generate context-specific insights on an ongoing basis, operating on the firm’s unified regulatory data (RGD) model.

This architecture is consistent with broader industry moves towards granular, data-centric regulatory reporting, where consistency of underlying data models becomes a prerequisite for automation at scale. In this context, Ascend’s reliance on a single data foundation is intended to support both institutional reporting and supervisory consumption without duplication or transformation across systems.

The integration of RSH into the Ascend platform extends this model to regulators. Workflow agents are applied across supervisory processes such as data collection, validation and examination, while analytics agents surface key risk indicators and interpret outputs ranging from granular data points to narrative reports. The result is a more continuous oversight approach, where supervisory activities are embedded into the same data and workflow infrastructure used by reporting firms.

Regnology frames this as a step towards its long-standing Straight-Through-Reporting (STR) vision, in which regulatory reporting becomes a largely automated, end-to-end process supported by standardised data, embedded controls and real-time analytics. The addition of agentic AI introduces a layer of orchestration that can dynamically adjust workflows and prioritise risk signals, rather than relying on static reporting cycles.

“Our position at the nexus of risk, regulation, and finance gives Regnology a unique vantage point to support the industry’s evolution,” said Rob Mackay, CEO of Regnology. “Our next-gen Ascend platform is the engine of a paradigm shift transforming compliance into a single strategic command centre where high-quality data, continuous insight, and intelligent orchestration converge.”

The emphasis on convergence between reporting and supervision is notable. By extending the same AI-enabled infrastructure to both sides of the regulatory relationship, the platform aligns with emerging supervisory expectations around data lineage, transparency and near real-time access to granular information. This mirrors regulatory initiatives globally that are moving away from template-based submissions towards direct consumption of firm-level data.

“Ascend was designed as the foundation for a new era of regulatory reporting —bringing automation, transparency, and intelligence to the core of the financial operating model,” said Linda Middleditch, Chief Product Officer at Regnology. “By extending agentic AI to both regulators and the regulated on a trusted RGD data backbone, we empower the industry to move from reactive reporting to continuous intelligence for faster decisions and more resilient oversight.”

Regnology said it will progressively migrate its broader solution set onto the Ascend platform, indicating a longer-term strategy to consolidate its regulatory reporting, risk and finance capabilities within a single, AI-enabled architecture. For firms, the practical implication is a continued shift towards platforms that combine data standardisation, workflow automation and supervisory alignment—reducing fragmentation across reporting regimes while increasing expectations around data quality and control.

Bloomberg Expands MAC3 Risk Models for Enhanced Portfolio and Risk Forecasting Across Public and Private Investments

Bloomberg has expanded its MAC3 multi-asset risk models to cover private markets, extending the platform’s portfolio and risk forecasting capabilities beyond traditional public asset classes into private equity, private credit, real estate, infrastructure, hedge funds and liquid alternatives. The update reflects growing demand among institutional investors for more consistent measurement of risk across portfolios spanning both public and private investments. Bloomberg presents the expansion as a way to bring those exposures into a broader portfolio risk framework.

“Institutional investors are increasingly allocating across both public and private markets, yet risk is often measured in silos,” said Jose Menchero, Head of Portfolio Analytics Research at Bloomberg. “With these new models, MAC3 delivers a consistent, cross-asset factor framework that enables Bloomberg clients to understand and manage risk seamlessly across their entire portfolio in an increasingly complex investment landscape.”

Bloomberg MAC3 is a multi-asset class risk factor model that combines quantitative research techniques with Bloomberg security data to provide institutional investors with a unified view of risk across the portfolio. The platform currently includes more than 3,000 individual risk factors and supports risk forecasting, risk attribution, performance attribution, stress testing and optimization. The model also offers six time horizons, ranging from a responsive daily model to a stable long-term model, giving firms flexibility to align risk forecasts with different investment decision-making processes.

The new private markets capability adds MAC3 models for private asset funds, hedge funds and liquid alternative funds, allowing investors to forecast and decompose risk more consistently across public and private markets and support a total portfolio view across asset types. Bloomberg says the private fund model is constructed using dedicated private-asset factors and data on approximately 50,000 private funds covering private equity, private credit, real estate and infrastructure strategies, alongside hedge funds and liquid alternatives.

Across the alternatives fund suite, the models capture exposures across strategies, regions, sectors, styles and key macro sensitivities including rates, commodities, volatility and FX. Bloomberg says this can help investors identify shared risk drivers across managers and strategies, supporting portfolio construction, risk budgeting and governance at total portfolio level. Bloomberg’s MAC3 risk models are available to Terminal subscribers, who can use them to explore portfolio risk across public and private assets. Bloomberg PORT Enterprise customers can also license the underlying risk data, including risk factor exposures, volatilities, correlations and historical returns, with programmatic access available via API.

More broadly, Bloomberg positions MAC3 and PORT Enterprise as part of its wider buyside solutions suite, spanning research management, order and execution management, portfolio and risk analytics, trade compliance and operations. In that sense, the private markets expansion extends Bloomberg’s effort to support cross-asset investment workflows through a common data and analytics foundation.

FIX Trading Community Urges Regulatory Alignment in Response to FCA Consultations

The FIX Trading Community has called for significant changes to UK financial regulation in its formal response to FCA consultations on the UK consolidated tape and transaction reporting. Executive Director Jim Kaye emphasised that harmonising UK reporting rules more closely with EU standards would reduce complexity, lower the reporting burden, and improve the quality of market data. By addressing current concerns with post-trade transparency, the association aims to boost investor confidence in UK-based liquidity.

Regarding the 2027 equities consolidated tape, FIX recommends a single provider to ensure a “single source of truth.” Key proposals include aligning off-venue transparency exemptions with off-book exchange trades, removing duplicative reporting for trades already captured by EU Approved Publication Arrangements, and introducing disclosures for trade execution methodology. The association also seeks clearer regulatory guidelines for order chains and cross-border transactions to eliminate ambiguity in reporting responsibilities.

On transaction reporting, FIX advocates for a pragmatic approach to data sourcing, including the use of Legal Entity Identifiers for trusts and the FCA’s FIRDS as a primary data source. The response suggests removing specific RTS 22 fields while preserving essential transparency data. However, the association cautioned that proposed changes to data points, such as DEA indicators, may require significant system upgrades for firms. Overall, the recommendations focus on simplifying logic and maintaining data quality to support market integrity.

Shield Advances Archive Modernization with Tiering and Migration Enhancements

Shield has updated its archive platform with a set of enhancements aimed at addressing a long-standing operational problem for financial institutions: how to modernise legacy communications archives without introducing migration risk, increasing storage costs or weakening compliance controls.

This latest release focuses on three areas – storage optimisation, large-scale migration and expanded governance – reflecting the practical constraints firms face as data volumes grow and regulatory expectations around record-keeping tighten.

At a technical level, the introduction of intelligent storage tiering signals a more granular approach to managing retained communications data. Frequently accessed records remain immediately searchable, while less active data is shifted to lower-cost storage tiers without losing metadata visibility or regulatory accessibility. The model is designed to reduce long-term retention costs while maintaining readiness for audit and investigation, including alignment with requirements such as SEC Rule 17a-4, CFTC, FINRA and MiFID II.

Migration remains a primary blocker to archive modernisation, particularly where firms face risks around data loss, broken lineage and evidentiary gaps. Shield positions its approach around enterprise-scale transfer with built-in validation and reconciliation to preserve completeness and auditability. The platform supports multi-petabyte migrations across legacy and modern environments, with deployments cited at over 9 petabytes and tens of billions of records migrated across multiple systems. Processing throughput is designed to operate at industrial scale, enabling firms to execute migration programmes within defined timelines rather than prolonged, multi-year transitions. Post-migration, data is maintained as a consistent and defensible record, supporting downstream compliance, audit and investigative workflows.

“Archive modernization has been constrained by migration risk, rising long-term costs, and limited control over data,” said Ofir Shabtai, CTO, Shield. “Shield Archive removes these barriers with a connected, AI-enabled data layer – combining proven large-scale migration with more efficient retention and enhanced governance controls that give compliance and legal teams greater visibility, control, and confidence in their data.”

Alongside storage and migration, the update expands governance and legal hold capabilities. This includes greater visibility into preserved data, bulk policy management and self-service controls for legal holds – features intended to reduce operational friction and improve responsiveness during investigations. AI-driven functionality is also introduced to support faster identification and assessment of relevant records.

These enhancements sit on top of a cloud-native architecture with immutable, write-once-read-many (WORM) storage, policy-driven retention and full auditability across the data lifecycle. The emphasis on single-tenant deployment and data accessibility reflects continued regulatory focus on control, traceability and evidentiary integrity.

The update builds on Shield’s positioning in digital communications governance and archiving, where archive platforms are increasingly expected to function as more than passive storage. As regulatory scrutiny intensifies and data volumes expand, the archive is becoming a foundational layer for surveillance, investigation and AI-driven analysis – rather than simply a system of record.

MAS Launches MindForge Toolkit, Expands BuildFin.ai Collaboration on AI Risk

The Monetary Authority of Singapore (MAS) has operationalised its approach to artificial intelligence (AI) governance in financial services, publishing a new AI Risk Management Toolkit developed through industry collaboration under Project MindForge. The initiative brings together 24 banks, insurers and capital markets firms, reflecting a coordinated effort to translate high-level AI principles into practical implementation frameworks.

At the centre of the release is an AI Risk Management Operationalisation Handbook, designed to provide firms with actionable guidance on embedding AI risk controls across both traditional and emerging use cases, including generative and agentic AI. The toolkit focuses on how institutions can implement and evidence existing risk management expectations in production environments.

The handbook is structured around four core areas aligned with MAS’ proposed AI risk management guidelines. These include governance and oversight, where firms are expected to define clear accountability for AI systems; risk management practices, including use-case identification and materiality assessment; lifecycle controls spanning development through deployment and monitoring; and a set of organisational enablers, such as data infrastructure and internal capabilities required to sustain responsible AI adoption.

Accompanying the handbook is a set of industry case studies, offering insight into how financial institutions are approaching AI deployment in practice. These examples highlight the operational challenges associated with scaling AI safely, particularly as firms move beyond experimental use cases into business-critical applications. They also reflect the growing need to integrate AI governance into existing risk and compliance frameworks rather than treating it as a standalone discipline.

The publication comes as MAS continues to review feedback on its earlier consultation on AI risk management guidelines, suggesting that supervisory expectations in this area are still evolving. In this context, the toolkit can be seen as an intermediary step—bridging policy intent and operational execution—while allowing both regulators and firms to iterate on best practices.

MAS has indicated that the handbook will be updated over time as industry adoption matures and supervisory expectations become more defined. This iterative approach recognises the pace of change in AI technologies, particularly with the emergence of more autonomous and agent-based systems that introduce new categories of model and conduct risk.

To support ongoing development, MAS will establish an industry workgroup under its BuildFin.ai initiative, bringing together MindForge participants and additional stakeholders. The group is expected to focus on developing implementation resources, sharing practical experience, and advancing approaches to managing risks associated with newer AI paradigms.

Commenting on the initiative, Kenneth Gay, Chief FinTech Officer at MAS, said: “The development of the MindForge AI Risk Management Toolkit, including the release of the Operationalisation Handbook, marks a major step forward in our journey to ensure the responsible adoption of AI in finance. We are committed to fostering a culture of continuous engagement and strengthening of AI governance and risk management practices across the industry. The BuildFin.ai programme also serves as a foundation for our next phase of collaboration in AI risk management, to bolster the safe adoption of AI across the financial industry.”

Taken together, the toolkit and associated industry collaboration point to a shift towards more collaborative, governance-led approaches to AI. As deployment extends across front-to-back workflows, the ability to operationalise risk controls, and evidence their effectiveness to supervisors become core conditions for deploying AI safely and credibly at scale.

ThetaRay and Matrix USA Target AML’s ‘Last-Mile’ Modernisation Challenge

Financial institutions facing intensifying regulatory expectations around anti-money laundering (AML) analytics are exploring ways to introduce advanced detection capabilities without dismantling long-established compliance infrastructures. A newly announced partnership between ThetaRay and Matrix USA targets this challenge, positioning AI as an overlay rather than a wholesale replacement for existing transaction monitoring systems.

Regulatory momentum is accelerating the shift. Initiatives led by the U.S. Financial Crimes Enforcement Network (FinCEN), alongside the European Union’s incoming Anti-Money Laundering Regulation (AMLR) and the creation of the Anti-Money Laundering Authority (AMLA), are pushing firms toward more sophisticated analytical approaches. Supervisory expectations increasingly emphasise demonstrable effectiveness in detecting financial crime rather than simple adherence to procedural compliance frameworks.

For many banks and FinTechs, however, the practical constraint lies in the architecture of their current AML systems. Transaction monitoring environments often rely on rules engines that have evolved over decades and underpin mission-critical compliance programmes. Replacing them outright can be operationally risky and prohibitively expensive.

The partnership between ThetaRay and Matrix USA is framed around that operational reality. Matrix USA brings experience integrating AML and financial-crime technology across global banking environments, including firms operating hybrid or legacy infrastructures. ThetaRay contributes its Cognitive AI detection engine and investigation tooling, designed to operate alongside existing controls.

“Banks want to modernize, but many operate mission-critical AML programs that were built over decades,” said Lior Blik, CEO of Matrix USA. “This partnership gives them a practical path forward: enhance their current systems with AI, adopt better analytics, and meet regulatory expectations—without rebuilding their entire stack.”

The joint approach centres on deploying machine-learning-driven scoring and anomaly detection as an additional analytical layer on top of existing rules-based monitoring platforms. Rather than replacing legacy systems, the objective is to augment them with behavioural analytics and automated investigation capabilities.

“As global AML standards evolve, institutions need partners who understand both the legacy landscape and the new AI-powered future,” said Idan Keret, Chief Revenue Officer at Matrix USA. “ThetaRay’s AI combined with Matrix’s delivery expertise allows banks to strengthen detection, reduce investigation workload, and move forward with confidence without throwing away their original investments.”

Industry conversations around AML transformation frequently reflect similar operational constraints. According to ThetaRay executives, banks increasingly want faster paths to modernisation that avoid multi-year technology rebuilds.

“Every conversation we’re having with banks right now comes back to the same issue: they don’t have time for another multi-year AML transformation. What they need is speed, certainty, and proof that AI can deliver results inside the systems they already run. This partnership is built around that commercial reality,” said Jeff Otten, Chief Revenue Officer at ThetaRay.

The broader strategic question, however, is not whether artificial intelligence will play a role in financial-crime compliance, but how it is deployed in a way that remains transparent, accountable and regulator-aligned.

“AML is entering its next phase. The question is no longer whether AI belongs in financial crime compliance, but how responsibly and effectively it’s deployed at scale. Partnerships like this are what turn innovation into infrastructure,” said Brad Levy, CEO of ThetaRay.

In practical terms, the collaboration focuses on combining ThetaRay’s AI-driven detection capabilities and investigation tooling with Matrix’s implementation and integration expertise. The goal is to enable banks to introduce machine-learning analytics, automate elements of alert investigation, and reduce false positives while preserving existing compliance platforms.

In AML compliance, the “last mile” is the operational stage where alerts generated by monitoring systems must be converted into defensible investigative outcomes – encompassing alert prioritisation, investigation workflows, analyst decision support, and ultimately suspicious activity reporting. Layering new analytical capabilities onto established rules-based systems offers institutions a pragmatic way to strengthen this stage of the compliance process while preserving existing technology investments, and helping firms prepare for evolving AML supervisory expectations across the U.S. and Europe through 2026.

ACA Expands Cross-Asset Transaction Cost Analysis with FXT Acquisition

ACA Group has acquired foreign exchange analytics specialist FX Transparency (FXT), extending its capabilities in transaction cost analysis (TCA) and best-execution monitoring within the FX market.

The move follows ACA’s 2025 acquisition of Global Trading Analytics (GTA), which marked the firm’s initial expansion into TCA across equities, fixed income, derivatives and foreign exchange. With the addition of FXT, ACA deepens its coverage in FX — one of the largest and most liquid global markets, but also one where execution quality can be difficult for institutional investors to assess.

Founded in 2009 and headquartered in Framingham, Massachusetts, FXTT has developed a reputation for independent, data-driven analysis of FX execution. Its analytics draw on a substantial repository of institutional trading data to help asset managers, pension funds, endowments, mutual funds, insurance companies and corporations evaluate trading performance and demonstrate fiduciary oversight.

The acquisition strengthens ACA’s broader push to combine governance, risk and compliance (GRC) expertise with trading analytics. As regulatory expectations around best execution continue to tighten across multiple jurisdictions, firms are under increasing pressure to demonstrate how they source liquidity, evaluate counterparties and measure execution quality across asset classes.

In foreign exchange markets, those demands can be particularly complex. Fragmented liquidity, varied execution methodologies and the decentralized market structure mean institutional investors increasingly rely on specialised analytics to evaluate trading outcomes and benchmark counterparty performance.

“The acquisition of FX Transparency represents a deliberate next step in building a best-in-class, cross-asset TCA platform,” said Patrick Olson, CEO of ACA Group. “Following our successful acquisition of GTA, we identified FXT as a complementary platform that brings recognized FX expertise, a strong institutional client base, and differentiated analytics that enhance our ability to support clients’ transaction cost analysis and best execution needs.”

FX Transparency’s leadership sees the deal as a way to extend its analytics capabilities within a broader governance and compliance framework.

“Joining ACA enables us to continue delivering the high-quality foreign exchange analytics our clients expect, now supported by ACA’s global operating resources and broad GRC expertise. Together, we can provide a comprehensive TCA solution that addresses the evolving needs of global institutional investors,” said John Galanek, Co-Founder and CEO of FX Transparency.

As institutional investors face growing scrutiny around trading transparency and fiduciary accountability, cross-asset analytics platforms that combine market data, execution analysis and compliance oversight are becoming core components in the GRC toolkit.

FinScan and Nexus AML Partner to Scale Data-First AML Operations

Financial institutions have long invested heavily in transaction monitoring, sanctions screening, and KYC controls, yet many compliance teams continue to confront a more fundamental obstacle: the quality of the data that feeds those systems. Poorly structured or incomplete customer and transaction data can generate large volumes of alerts and manual investigation work, stretching already pressured financial crime teams.

Against this backdrop, Innovative Systems’ FinScan has entered into a strategic partnership with Nexus AML aimed at addressing the operational bottleneck at the front end of AML programmes: data readiness. The collaboration brings together FinScan’s data cleansing and sanctions-screening technology with Nexus AML’s outsourced financial crime operations, reflecting a growing industry emphasis on “data-first” compliance architectures.

Operational pressure linked to data quality is widely recognised across the sector. In a FinScan poll of 550 compliance professionals, 59% reported that data quality consumes most of their time – highlighting the extent to which AML teams are still managing downstream consequences of upstream data issues.

The partnership is designed to address that imbalance by combining automated data preparation with operational expertise in clearing and managing alerts. Institutions working with Nexus AML will be able to integrate FinScan’s real-time data cleansing and screening technology, which targets sanctions, watchlists and payment flows, with the aim of reducing unnecessary alerts and improving the accuracy of screening results. In parallel, organisations using FinScan’s technology will be able to access Nexus AML’s operational support services to help manage investigation workloads and maintain compliance processes during periods of heightened activity.

Deborah Overdeput, Chief Operating Officer at Innovative Systems, framed the collaboration around the operational foundations of AML programmes. “FinScan and Nexus AML share the belief that financial crime compliance must be both operationally scalable and strategically grounded in high-quality, compliance-ready data,” she said. “Our partnership brings together two complementary strengths: operational excellence in clearing and investigating alerts, and a data-first screening approach that reduces those alerts in the first place. Together, we’re helping institutions build AML programs that are more efficient, defensible, and sustainable.”