AI is reshaping how regulated financial institutions approach compliance, surveillance and operational resilience, but its effectiveness depends entirely on the quality of the data it can access.
As firms accelerate the adoption of advanced analytics, machine learning and natural language processing, many are discovering that fragmented communications records remain a fundamental barrier to unlocking real AI-driven value. Incomplete, siloed or poorly governed data creates blind spots that not only weaken compliance oversight, but also limit the operational benefits AI promises to deliver.
Against this backdrop, Verint and Wordwatch are bringing together industry expertise in a joint webinar focused on one of the most persistent challenges in financial services: building a reliable data foundation that enables AI insights to function as intended. The session, titled Fragmented records to AI insights: building the data foundation for AI value generation, will take place on Wednesday, 28 January 2026 at 10.00am GMT (11.00am CET), and will explore how organisations can unify, preserve and govern communications data across increasingly complex environments.
The regulatory landscape is a central driver behind this discussion. Financial institutions are operating under strict and continuously evolving frameworks, including MiFID III, CP25/18 and GDPR. These regulations place heavy demands on firms to capture, retain and supervise communications data in a way that is both comprehensive and auditable. Failure to do so exposes organisations to regulatory risk, enforcement action and reputational damage, particularly as regulators intensify their scrutiny of digital communications and conduct oversight.
At the same time, the communications landscape itself has become far more complicated. The rapid expansion of digital channels, widespread adoption of unified communications tools and the normalisation of hybrid working models have significantly increased the volume and variety of data that firms must manage. Voice, video, messaging and collaboration platforms now sit alongside traditional channels, creating operational complexity and making it harder to maintain consistent compliance controls.
Within this environment, AI is increasingly viewed as a game changer for risk and compliance teams. Secure and explainable AI can enhance speech transcription, analytics and insight generation, helping firms identify potential conduct issues earlier and at greater scale. However, without complete and well-governed data, even the most sophisticated AI models struggle to deliver meaningful results. Poor data quality limits accuracy, undermines explainability and reduces trust in automated insights.
This is where the data foundation problem comes into sharp focus. Many organisations continue to rely on siloed legacy recording systems that were never designed to support AI-driven use cases. Incomplete datasets and outdated infrastructure not only block AI effectiveness, but also increase operational risk. Decommissioning these systems securely and consolidating data into a unified environment is a critical step toward enabling AI to add measurable business value.
The webinar will also examine how the Wordwatch Compliance Platform addresses these challenges by providing a unified communications governance solution built on a truly compliant archive. By creating a single source of truth for all communications data, firms can improve oversight, streamline compliance workflows and lay the groundwork for advanced analytics. In parallel, Verint’s Financial Compliance AI Insights, including Conduct Risk, Activity Summary, and Notes and Actions Insights, demonstrate how AI can be applied to proactively mitigate risk while improving operational efficiency.
The session will be led by Nigel Cannings, senior director, GTM & compliance solutions at Verint, alongside William Davenport, managing director at Wordwatch, offering practical perspectives on how regulated firms can move from fragmented records to AI-driven compliance intelligence.
Register for the webinar here.
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