VoxSmart Unifies Communications Compliance Workflows in VX1

VoxSmart has launched VX1, a communications compliance platform that brings mobile capture, surveillance, investigations, archiving, alert detection and voice transcription into a single interface.

The platform is designed for investment banks, hedge funds and asset managers monitoring communications across voice, mobile and digital channels. The announcement cites WhatsApp and Bloomberg chat among the sources contributing to growing data volumes and fragmented compliance processes.

VX1 consolidates communications data and workflows that may otherwise sit across separate capture, surveillance and investigation systems. VoxSmart said this unified approach will help compliance teams investigate communications linked to potentially suspicious trading activity.

The platform also embeds artificial intelligence (AI) within alert management and investigation workflows. VoxSmart describes its approach as human-led: AI can help analysts summarise, prioritise and investigate alerts, while people retain responsibility for compliance decisions.

“Firms are already seeing the greatest value from AI when it helps analysts summarise, prioritise and investigate alerts more effectively, rather than attempting to replace their judgement,” said Oliver Blower, chief executive officer of VoxSmart.

VoxSmart argues that bringing capture, communications data and investigation workflows together provides a more complete foundation for AI-assisted surveillance. Fragmented data could leave models and analysts working from an incomplete record, even where the surveillance technology itself is sophisticated.

“When communications are fragmented across different channels and systems, firms risk building increasingly sophisticated surveillance on top of incomplete foundation,” Blower said. “VX1 brings that data and those workflows together, giving firms the visibility and control they need to use AI confidently, while ensuring every decision remains explainable and accountable to a human.”

Shield Connects Live Surveillance Alerts to Enterprise AI Tools with MCP

Shield has made its Model Context Protocol (MCP) Server generally available, allowing authorised compliance teams to query live surveillance alerts through supported enterprise AI tools. Claude is the first supported AI environment, with further integrations planned.

The server provides a permissioned, read-only route between an AI interface and Shield Surveillance. Shield remains the system of record, while each request inherits the user’s existing access rights. This prevents the AI tool from exposing alerts that the user is not authorised to view.

Users can query alert information across the communications channels and data sources captured by Shield. Available information includes alert volumes, service-level agreement status, risk scores, ownership and investigation status. The server can return data in structured formats and visualise it within supported AI environments.

A compliance officer could ask the AI tool to identify the week’s highest-risk alerts or break down the alert queue by status and reviewer workload. The user does not need to leave the AI interface or open a separate Shield assistant to retrieve the information.

The release extends Shield’s existing governance of AI communications. Its platform already captures and monitors interactions conducted through AI tools. The MCP Server adds access in the other direction by making Shield’s surveillance information available within approved AI environments.

“The future is not another proprietary compliance assistant,” said Tamar Sharir Beiser, chief product officer at Shield. She said firms should be able to bring their existing compliance platforms and intelligence into their chosen AI environments under governed access.

The first release is limited to alert information and does not permit users or AI tools to change records or initiate actions. Shield plans to extend the connection to search, case management and communications review. Later releases are also expected to support workflow actions.

Shield cited research showing that integration remains an obstacle to enterprise AI adoption. The company’s approach uses the open MCP standard to connect AI tools with an established surveillance platform while retaining existing permissions and controls. For compliance teams, the implementation separates access through an AI interface from control over the underlying surveillance record.

CUBE Adds Agentic Coworkers to Embed Regulatory Intelligence into Enterprise Wide Workflows

CUBE has added three agentic artificial intelligence tools to its RegPlatform regulatory intelligence platform, extending its automation across regulatory change monitoring, analysis and enforcement tracking.

The Priorities, Analysis and Enforcements “coworkers” are available to existing RegPlatform customers. Application programming interface access allows firms to embed the tools within their existing compliance and risk systems rather than operate them as separate applications.

Priorities Coworker assesses incoming regulatory developments for their relevance to each organisation. It surfaces updates requiring attention while deprioritising those unlikely to require action. CUBE said early customer testing showed an 80% to 90% reduction in effort spent reviewing irrelevant developments.

The company’s 2025 Cost of Compliance Report found that 82% of surveyed firms track between 26 and 100 regulatory developments each month. However, 45% said fewer than half of those developments required action, while one fifth said the proportion was below 25%.

Analysis Coworker allows compliance teams to question applicable updates, laws and regulations using natural language. It returns structured answers based on the regulatory intelligence held for that organisation, reducing the manual work involved in searching and cross-referencing source material.

Enforcements Coworker brings fines and regulatory notices from multiple jurisdictions into a continuously updated view. It extracts related risk themes and obligations and categorises enforcement pressure by regulator, topic and severity. This could help firms connect regulatory developments with their own risk and control priorities, although the announcement does not specify how recommendations are reviewed or approved.

“Compliance and risk teams at financial services firms have always had access to regulatory intelligence from CUBE. What the agentic coworkers provide is the ability to filter any noise with incredible precision, answer compliance and risk team questions, and perform the repetitive work – all of which is delivered proactively within customer’s existing systems and operating models,” said James Mackonochie, CUBE’s Global Executive Head of Product.

The agents operate within RegPlatform’s existing Microsoft Azure infrastructure and are available through Microsoft Marketplace. CUBE said integration into a customer’s infrastructure typically takes less than two weeks and requires no changes to existing infrastructure.

KOR Launches Australian Trade Repository for ASIC Derivatives Reporting

KOR has begun accepting submissions through its Australian derivative trade repository (ADTR), giving reporting entities a second licensed repository option for complying with the Australian Securities and Investments Commission’s (ASIC’s) Derivative Transaction Rules.

The repository supports all reportable asset classes and client segments. Its launch follows ASIC’s decision to grant KOR an ADTR licence, making Australia the latest market in which KOR operates licensed trade repository infrastructure.

The opening comes as ASIC increases its scrutiny of derivatives reporting following the revised rules introduced in October 2024. KOR said regulatory fines and data-quality assessment reports have placed greater pressure on firms to improve the completeness and accuracy of their submissions.

KOR’s repository provides sub-second message processing, aggregated explanations for rejected messages and real-time grouping of rejections. The company said these functions can help reporting teams identify systemic submission problems and understand how rejected reports should be corrected. Clients can also access reporting histories on demand and test their submissions in an environment designed to operate at production-level performance.

“A reporting team should be able to have a well explained reason for why a message was rejected and how to correct it, have a TR that is always open to receive data, and a support staff who are experts in the regulations,” said Jonathan Thursby, Chief Executive Officer of KOR. “That is not an ambitious standard for a trade repository. It is the baseline.”

Firms moving open trades or positions to KOR must send a port-out message to their incumbent repository and a corresponding port-in message to KOR. The same unique transaction identifier (UTI) is retained, avoiding the need to re-key trades or reconstruct their reporting history. Closed trades and positions remain at the previous repository. KOR said it will manage the porting process for users of its reporting services.

The Australian operation extends KOR’s licensed trade repository coverage beyond the United States, where it operates repositories regulated by the CFTC and SEC, and Canada. Its wider reporting platform also supports obligations in the EU, UK and Singapore.

Confluence Embeds AI Validation and Conversational Controls in POINT Launch

Confluence Technologies has launched Confluence POINT, an artificial intelligence-enabled layer designed to automate work surrounding its regulatory reporting, analytics and investor communications products.

The first capabilities address two distinct parts of the investment-management workflow. POINT Validation reviews financial and regulatory reporting documents, while a conversational interface allows users of Confluence Revolution to query data and initiate processes using plain English.

POINT Validation can process structured and unstructured documents across multiple formats. It applies an automated checklist, records an audit trail for each finding and flags errors for follow-up. Confluence said the process can be completed within minutes, reducing the manual checks that often follow automated report production.

The capability extends automation beyond generating a report. Validation remains a separate control, but POINT is intended to perform the initial review and direct users towards exceptions requiring attention. The audit trail should also give firms a record of what the system checked and which issues it identified.

Within Revolution, Confluence’s multi-asset performance, attribution and risk platform, users can ask questions or start processes through a conversational interface. The interface is also available through Microsoft Excel, allowing users to work with Revolution data and functionality without exporting information or moving between systems.

Confluence plans to add further POINT capabilities across its product suite. Mark Evans, founder and chief executive officer, said: “This is a long-term commitment, and you will see our AI capability continue to grow across our entire product suite in the months ahead.”

The initial release therefore applies embedded AI to two different operational problems: document validation and access to analytics. Its practical value will depend on the quality of the automated checks, the usefulness of the audit records and the controls governing actions initiated through natural-language instructions.

Napier AI and Delta Capita Link KYC and AML Workflows

Napier AI and Delta Capita have formed a partnership that combines client onboarding and Know Your Customer (KYC) processes with anti-money laundering (AML) screening and transaction monitoring.

The companies will connect Delta Capita’s Karbon client lifecycle management platform with Napier AI Continuum. Napier’s contribution covers client screening, transaction monitoring and transaction screening. Delta Capita will provide KYC technology, advisory expertise and managed services.

The combined proposition is intended to support financial institutions from initial onboarding through ongoing monitoring and investigation. It targets a common operational problem: KYC and AML functions often rely on separate systems, teams and processes, creating manual handovers and duplicated work.

According to the announcement, connecting these functions could reduce onboarding delays and investigation volumes. The companies also expect the partnership to help institutions lower false positives, establish more consistent processes and maintain clearer audit trails. The release does not provide performance data or implementation examples supporting these expected benefits.

Greg Watson, chief executive officer of Napier AI, said: “Financial institutions are under pressure to manage increasingly complex fincrime risks while simultaneously deliver faster, more accurate and seamless experiences for customers. Our partnership with Delta Capita brings together complementary technology and expertise to give institutions a more connected approach across the compliance lifecycle. By reducing manual handovers, this enables institutions to focus their resources on genuine risk, meaning they can achieve stronger customer, operational and regulatory outcomes.”

The partnership also gives institutions the option to combine technology with advisory and outsourced operational support, rather than procuring each component separately. The companies said this would allow firms to adapt the service to their operating requirements.

Sean Vickers, CLM Chief Commercial Officer and Global Head of CLM advisory at Delta Capita, said “Institutions crucially must understand who their customers are and maintain this understanding as relationships and risks evolve. Through our partnership with Napier AI, combining Continuum with our Karbon platform, we’re able to create a compelling offering for institutions to transform both their systems and operating models, simplifying complex processes, improving processes and building compliance operations that are more efficient, accountable and scalable.”

Bloomberg Vault Adds AI Models for Insider Dealing and Personal Trading Surveillance

Bloomberg has expanded Bloomberg Vault with two artificial intelligence models designed to identify electronic communications that may point to insider dealing or breaches of employee personal trading policies. The additions extend the platform’s surveillance coverage to two risk areas that can require compliance teams to examine communications alongside trading and account activity.

The Insider Dealing AI Policy looks for communications that may indicate the improper acquisition, disclosure or use of material non-public information (MNPI). The Personal Trading AI Policy targets communications concerning employees’ trading activity and investment accounts, including possible failures to comply with firms’ internal personal trading requirements.

The models join Bloomberg Vault’s existing suite of AI-powered surveillance policies, which covers market conduct, non-market conduct and conflicts of interest. Bloomberg said the expanded suite is intended to help compliance teams identify potential risks within electronic communications and concentrate their reviews on more relevant alerts.

Each model has been built for a defined risk scenario and is supported by documentation explaining its construction, risk focus and intended behaviour. According to Bloomberg, large language models are applied following model inference to improve the relevance of alerts and reduce false positives.

The documentation is also intended to help firms assess how the models operate within their AI governance frameworks. This is particularly relevant where compliance teams must explain the basis for surveillance alerts and evaluate externally supplied AI systems before deploying them in regulated workflows.

“Bloomberg’s AI-powered surveillance models have substantially improved alert quality and reduced false positives,” said Jotham Banyikidde, Investment Compliance Senior Associate at Impax Asset Management. “This allows our team to focus on meaningful review and oversight. Just as importantly, the models are transparent and well documented, which supports our internal AI impact assessment and gives us confidence in how alerts are generated.”

The release follows Bloomberg’s introduction of BSpeech, its AI-powered, multilingual voice transcription service. Bloomberg said the combination extends Vault’s AI capabilities across electronic and voice communications, allowing firms to manage surveillance and communications governance across multiple channels through a single workflow.

Comply Adds Kalshi Data to Employee Trading Surveillance Platform

Comply is adding Kalshi contract-trading data to its compliance platform, allowing financial firms to monitor employees’ prediction-market activity alongside securities, futures, options, cryptoassets and other investments.

The integration extends existing employee-trading controls to a market that may fall outside firms’ traditional personal account dealing programmes. Compliance teams will be able to configure monitoring rules and pre-clearance requirements, analyse trading patterns and investigate potential policy breaches through Comply’s case-management workflow.

The platform will ingest Kalshi contract trades in real time, according to Comply. Firms can use the data to identify trades that employees have not disclosed and assess whether trading activity conflicts with internal policies governing material non-public information (MNPI).

Compliance teams will also be able to document investigations, findings and remedial action within the platform. Additional controls include employee certifications covering contract-trading policies and consulting support for risk assessments and policy reviews.

“Prediction markets have grown faster than most compliance frameworks were designed to handle,” said Michael Stanton, CEO of Comply. “Compliance teams don’t need a separate solution – with Comply, they can monitor contract trades alongside equities, bonds, options, futures, and crypto in a single platform.”

The Kalshi partnership broadens Comply’s existing coverage of prediction markets. The company also monitors trades conducted through Polymarket using data supplied under its partnership with cryptocurrency tax and accounting platform ZenLedger.

Max Crowley, vice president of business development at Kalshi, said financial services firms were “navigating new territory as prediction markets become mainstream”. He added that the partnership was intended to incorporate employee prediction-market activity into firms’ existing compliance programmes.

“Most firms are still figuring out what a reasonably designed prediction market compliance program looks like, and that’s exactly where we come in. Comply brings both the technology and the regulatory expertise to build programs that hold up under scrutiny,” said Jamila Mayfield, Chief Regulatory Service Officer of Comply.

Fenergo Launches Fen-AI to Govern AI Across Client Lifecycle Management

Fenergo has launched Fen-AI, an orchestration platform designed to help financial institutions deploy AI across client lifecycle management while retaining human oversight, policy controls and an auditable record of decisions.

The platform supports Fenergo’s KYRA family of AI agents, which can carry out tasks across client onboarding, periodic reviews, ongoing monitoring and material changes to client records.

Fenergo said Fen-AI was developed in response to financial institutions’ need to increase the capacity of Know Your Customer (KYC), Anti-Money Laundering (AML) and client lifecycle management (CLM) operations without weakening governance. The company works with more than 110 financial institutions, including over 40% of the world’s 50 largest banks.

“Financial institutions don’t have an AI problem. They have a scale problem. Risk moves in real time and regulation evolves continuously. Yet the work of compliance still depends on review cycles built for a slower world. Fen-AI changes that. We’re enabling institutions to move from periodic control to continuous control, delivering faster client onboarding, greater operational efficiency and stronger compliance without increasing risk or headcount.” Marc Murphy, Founder and CEO, Fenergo.

Fen-AI is built on Fen-X, Fenergo’s Legal Entity System of Record. Actions taken by KYRA agents, along with the underlying sources, decisions and rationale, are recorded as work is completed. This is intended to give firms a traceable account of how automated decisions were reached and provide evidence for internal assurance and regulatory review.

Fenergo describes this extension of Fen-X as a “Continuous System of Control”. Fen-X supplies client data, regulatory policy logic and decision evidence, while Fen-AI governs how AI agents access and act on that information. KYRA agents then perform tasks within controls established by the institution.

Connecting Agents Through a Governed Interface

Fen-AI includes an Agent-to-Agent (A2A) Interoperability Framework that allows institutions to connect Fenergo agents with approved internal and third-party agents.

The framework uses the Model Context Protocol (MCP) and A2A interoperability to authenticate requests, maintain client and workflow context during handovers and attribute completed actions to the relevant agent or system.

The results are written back to the Fen-X system of record and captured in what Fenergo describes as an immutable evidence trail. The approach is intended to allow firms to add new agentic capabilities without losing visibility over how work moves between agents, applications and data sources.

Fen-AI also provides operational reporting designed to show where agents are contributing capacity. Metrics include completed tasks, analyst hours returned, manual activity avoided, throughput, entities covered and workload by capability.

“AI in financial institutions will succeed only if it’s built on trust. Regulators will not accept ‘the AI decided’ as an answer. That is why we built governance into the foundation of Fen-AI from day one. Every action is attributable. Every decision is explainable. Every outcome is anchored to a trusted system of record. We are creating a new category for regulated industries: the governed agentic workforce.” Hishaam Caramanli, President and COO, Fenergo.

The first release of KYRA includes six agents focused on routine client-lifecycle tasks. Fenergo said further Fen-AI capabilities would be introduced in the coming quarters.

Fen-AI and the initial KYRA automation agents are available to Fenergo customers globally.

FinScan Completes LSEG Validation for World-Check On Demand Integration

FinScan has completed LSEG Risk Intelligence’s technical validation process for its integration with World-Check On Demand. LSEG has designated the integration as ready to go to market.

The integration gives financial institutions access to LSEG’s risk intelligence within FinScan’s anti-money laundering (AML) and sanctions-screening workflows. It is designed to reduce the operational work involved in connecting screening technology with sanctions and watchlist data.

“Our process follows rigorous technical validation to confirm that partner integrations align with World-Check On Demand’s architecture, data delivery standards, and operational best practices,” said Priya Nallan, Head of Product, Screening at LSEG Risk Intelligence. “With this milestone achieved, FinScan is now recognized as a technology partner capable of delivering World-Check On Demand within its AML and sanctions screening workflows.”

FinScan said the integration can support sanctions and watchlist screening across multiple regulatory regimes. Users can access World-Check On Demand data directly within FinScan’s screening workflows.

“This confirms that FinScan is fully aligned with LSEG’s integration standards and ready to deliver World-Check On Demand within our screening platform,” said Deborah Overdeput, Chief Operating Officer at Innovative Systems.

FinScan is among the first screening technology providers to complete the full World-Check On Demand validation process. The designation provides a potential early-mover benefit as financial institutions seek closer integration between external risk intelligence and their screening systems.

“We’re seeing significant interest from our joint teams and customers in solutions that combine powerful screening technology with trusted risk data,” added Overdeput. “This achievement ensures FinScan customers can confidently deploy a validated integration that aligns with LSEG’s standards and delivers the intelligence compliance teams depend on.”