AI solutions purpose built for regulated financial services contact centres.

The challenge.

In financial services contact centres, identity verification is one of the highest volume interactions and one of the most common points at which AI automation fails.

Many AI-enabled voice assistants never reach intent capture or self-service because they cannot reliably extract identity details - such as account numbers, postcodes, or dates of birth - from live customer speech.

When identity verification fails:

• The AI cannot securely access the customer record.
• No personalisation or transaction handling is possible.
• Automation stops before it starts.
• Calls escalate immediately to human agents, often before any value has been delivered by automation.

As a result, agents remain responsible for high-volume, low-value ID&V tasks, while more complex customer needs wait longer in queues. The expected productivity gains from automation fail to materialise and operational costs remain stubbornly high.

In regulated environments, ID&V is not just a compliance requirement - it is the gateway to all downstream automation. If it doesn’t work reliably in production, neither does AI.

What Connect enables.

AI that can actually complete identity verification on live calls.

General purpose AI models often struggle with the exact entities required for identity verification. Account numbers, postcodes and dates of birth are not conversational concepts, they are precise values, spoken imperfectly, often under poor audio conditions and frequently with pauses, corrections, or changes in pace.

When AI mishears even a single digit, identity verification fails. And when ID&V fails, automation stops immediately and calls are escalated to human agents.

Connect addresses this by using specialised micro-models optimised specifically for entity recognition in ID&V scenarios, including enhanced inverse text normalisation to handle how people actually speak numbers and identifiers on live calls.

This enables:

• Accurate capture of identity details from natural, imperfect customer speech.
• Deterministic, predictable handling of ID&V at customer-critical decision points.
• Secure self-service completion once identity is confirmed.
• Clean escalation to agents only when required, with full verification context preserved.

Operational outcomes.

Because identity verification works reliably:

• More callers are verified without an agent, removing one of the most time-consuming manual tasks in the contact centre.
• More journeys complete end-to-end with AI, including transactions and FAQs, not just front-of-journey triage.
• Agents stop spending the beginning of every call on ID&V and actually start where value begins, rather than repetitive ID&V.
• Queue pressure reduces, both because automation completes more journeys and because agents become available sooner for escalations that truly require human intervention.

The operational return is two fold:

1. Immediate savings from removing agent-led ID&V.
2. Compounding ROI as AI is finally able to handle full customer journeys and not just deflect initial contact.

Why this matters.

In financial services, ID&V is the control point that determines whether automation can progress or must stop. If AI cannot reliably handle identity verification in production, it cannot access the customer record, personalise interactions, or complete transactions, regardless of how “intelligent” it sounds in a demo.

When ID&V works, automation works and customer effort reduces, agents spend their time where it adds value, and trust and regulatory confidence are maintained.

This is why accurate, deterministic entity handling, not conversational fluency, is what makes AI viable in regulated contact centres.

This is where automation breaks down before it delivers value. Many contact centres attempt to automate high-volume servicing interactions such as balance queries, document requests, confirmations, simple changes - because the underlying tasks themselves are straightforward and highly repetitive.

In practice, automation often fails not because the intent is complex, but because AI cannot reliably understand real customer speech once calls move into production. Live contact-centre conversations are noisy, interrupted, accent-heavy and shaped by imperfect telephony. When transcription accuracy drops:

• Intents are misclassified.
• Critical details like dates, references or identifiers are misheard.
• Journeys stall or escalate early “just to be safe”.
• Agents are pulled back into work that should have been automated.

The result is familiar: automation that works in demos, but collapses under real call volumes.

What Connect enables.

Speech understanding that holds up in production.

Connect’s AI capability is designed around the reality that speech accuracy is the foundation of all downstream automation.

Rather than relying on large, general-purpose models, Connect uses task- and domain-specific speech and language models, optimised for contact-centre environments. These models are trained to handle:

• Diverse accents and natural speech patterns.
• Noisy, compressed telephony audio.
• Repetitive, speech-heavy servicing interactions.
• Accurate extraction of structured data from live calls.

This enables AI not just to recognise broad intent categories, but to consistently capture the exact information required to complete servicing journeys end-to-end.

Operational outcomes.

Because speech understanding holds up in production:

• Routine servicing interactions are completed by AI, not just triaged.
• Agent involvement is reduced where it adds no value, lowering cost-to-serve.
• Consistency improves, as requests are handled the same way every time.
Resilience increases during peak demand, because automation doesn’t degrade under volume.

Crucially, agents are no longer spending time correcting transcriptions or recovering high volumes of failed automations. Their time is focused on exceptions and complex customer needs where human judgement adds value.

Why this matters.

In regulated contact centres, automation only delivers value when accuracy holds under real conditions. If AI cannot consistently understand live customer speech, it becomes a risk control mechanism, escalating calls early, rather than a productivity engine.

Connect’s approach ensures that speech-driven automation is accurate enough to complete tasks, predictable enough to govern and reliable enough to scale across volumes.

This is what turns high-volume servicing from an automation aspiration into a dependable operational capability.

Connect designs AI to operate as a governed orchestration layer, not a collection of disconnected bots or channel specific automations.

This means:

• Identity is verified once, at the appropriate point in the journey.
• Intent, verification status, and customer context are preserved across steps and channels.
• Decisions and handovers happen at clearly defined, auditable control points.
• AI completes what it is authorised to handle and escalates only when policy or risk thresholds require it.

By orchestrating journeys end-to-end, rather than automating individual steps in isolation, Connect ensures that automation operates within clear operational and regulatory boundaries while maintaining continuity of customer experience.

Operational outcomes.

With governed orchestration in place:

• Repeat contact caused by broken or duplicated journeys is significantly reduced.
• Service consistency across voice and digital channels.
• Operational complexity decreases, even as channel choice expands.

Agent time is focused on cases that genuinely require judgement or intervention. This allows organisations to scale automation without increasing operational risk or burdening frontline teams with fragmented processes.