Europea Seguros: One AI Layer Across Sales, Service and Collections

Europea Seguros protects policyholders across health, life and funeral cover. Almost none of that happens in an app. It happens on the phone: the sale, the service call, the retention save, the chase for an unpaid receipt.

So the conversation is where three different business outcomes are decided. Rather than buy a QA vendor, a coaching vendor and a collections dialler, Europea deployed one AI layer over the same calls, with three capabilities on top. Conversion, retention and recovery now share a dataset: when one moves, Europea can see the conversation that moved it.

This is the broadest InfiniteWatch deployment by surface area: not one product in one team, but a single AI layer running across sales, customer and mediator service, and collections.

The Challenge: Three Numbers, One Phone Line

Europea set out to move three numbers at once, and all three are decided on calls nobody could see.

Conversion was uneven and unexplained. Across roughly ten sales agents split between Spain and Latin America, output varied by a factor of four: some closed 40 policies, others 10. Nobody could say what the top performer did differently, because nobody heard enough calls to know. Lead scoring did not exist, and the funnel defined nothing past Contacted.

Retention was the real problem, and it was invisible. Europea grows above sector average, so acquisition is not the issue. Churn is, and cancellation reasons were never captured. Renewals are automatic on December 31, so no renewal call is ever made, which means the last real conversation with a leaving customer is the one nobody reviewed.

Quality was a single post-call rating. Customer service had one KPI, the IVR score collected by the phone system after the call. No QA scorecard existed, call tagging was inconsistent, and supervisors were blind to what agents actually did.

Collections was volume the team could not absorb. Premiums are charged on a recurring cycle, and every cycle returns a batch of unpaid receipts in the days that follow, each one becoming a task for an agent to call. There was no standard process, no defined number of attempts and no reliable log: agents called, sometimes emailed, sometimes sent a WhatsApp, in no fixed order. Follow-up stopped when the office did, leaving the evening hours, when people actually answer, unworked. A meaningful share of the book that lapses does so through collections, much of it without a single real conversation.

A Concrete Example: The Receipt Nobody Called About

Before InfiniteWatch. A premium bounces. The return file is downloaded by hand the next morning, the receipt is flagged unpaid in the ERP, an automatic email goes out, and the case joins the rest of the batch from that cycle in an agent queue. The agent calls once, mid-afternoon, gets no answer, logs nothing. The next cycle brings a fresh batch and the first case is never reopened. Months later the policy cancels for non-payment. The customer never refused to pay. Nobody ever reached them.

With InfiniteWatch. The return triggers the AI Workers straight from the ERP instead of creating a queue item. The case is ranked by likelihood of payment and by what is at stake: the premium, whether other receipts are pending, whether this is the first return or the third. The Worker then works it across three to five attempts at varied times, including the evening slot no human staffed, escalating call to WhatsApp to email. It negotiates real options, splitting the amount or moving the charge date, sends the payment link, retries the charge, logs the commitment and follows up until the premium is recovered or the case is genuinely unreachable. Humans keep the disputes and the hard negotiations. The routine cases stop falling through the floor.

The Solution: Coach Live, Score Everything, Automate the Chase

AI Real-Time Coach sits with the agent while the call is happening, surfacing context, prompts and suggestions as it develops: a pet insurance cross-sell when a customer mentions their dog, or a line that keeps the conversation alive when someone says they will call back themselves. This is what after-the-fact QA cannot reach. A supervisor listening back can tell an agent what they should have said; they cannot tell them in time. Europea configures what the coach surfaces, so the guidance follows their playbook, not a generic script.

AI Voice Insights scores 100% of interactions: QA on every call rather than a sample, comparison between agents and teams, possible-fraud and churn-risk scores, five-level sentiment, and natural language questions over the whole dataset instead of fixed dashboards. The scorecard was built from the behaviour of Europea's own best agents, which turns the 40-versus-10 spread from a mystery into a template. When an agent disputes a score, the platform averages it and generates a coaching guide explaining the reasoning, and flagged misclassifications feed an automatic recalibration loop.

AI Workers for collections run the premium recovery cycle: detect returned receipts, rank the portfolio by likelihood of payment, reach out by call, WhatsApp and email, propose real options like splitting the amount or moving the charge date, retry the charge through the payment processor, log commitments and follow up until the premium is recovered or resolved. The ERP calls InfiniteWatch directly, so work starts when the return lands, not when an agent reaches the bottom of a queue.

Deployment: Four Weeks, Live Across the Business

Discovery started July 15, 2026. The full circuit went live on August 17.

• Week 1: telephony integration, the layer carrying all 128,287 calls
• Week 2: CRM integration for customer context. In the April to July baseline, 93.6% of calls matched a client record by phone number, giving the layer context on almost every conversation from day one
• Week 3: configuration MVP validated on a 200 to 300 call sample, with the QA scorecard built against Europea's top performers
• Week 4: testing, validation and team training
• Collections track: ERP integration for payment-link generation, status updates and receipt reissuance, plus the payment processor and WhatsApp as the payment and contact rails

Sensitive customer data is redacted automatically before analysis, so deeper insight never costs privacy or compliance.

What Europea Measures Now

At go-live, Europea moved from a single post-call rating to a full measurement frame across the three teams:

• Sales: policies per hour against a 0.12 target, conversion by lead scoring tier, churn attributable to each agent, and average LTV, so a high-volume seller who also generates high churn is finally visible as such
• Service: CSAT, QA score, possible-fraud score, churn-risk score from 0 to 100, and sentiment, on every call rather than a handful
• Collections: contact rate, promise-to-pay rate, and recovered premium per returned receipt, on cases that previously left no record at all

What comes next: load the historical call archive to set a pre-deployment baseline, extend the AI Workers to the welcome calls that verify new policies, and cover the after-hours window currently routed outside the company.