What reduces quote response time with an AI layer
To reduce quote response time for brokers with AI, a Brazilian P&C insurer (Seguros e Danos) does not rebuild its core. It adds an external AI layer on top of the systems it already runs, and that layer compresses the time between the moment a submission arrives and the moment the corretor (broker) gets an answer. The mechanism matters more than any promise. A quote returned while the broker is still in-flow tends to convert. A quote returned a day later arrives after a competing carrier has already shown its number.
Response speed is a primary commercial lever in this market, not a back-office nicety. According to Capgemini's World Insurance Report, 60%+ of brokers choose an insurer by response speed. The corretor controls distribution in Brazilian P&C and routinely shops the same risk to several carriers, so the one that returns a clean, consistent cotação (quote) first earns a structural advantage on attention rather than price. A visible service level on the response window compounds this. When a broker knows an insurer reliably answers within a stated window, more submissions route to it by default.
The case for an external layer rather than a core migration is straightforward. Deloitte finds underwriters spend 40% of their time on administrative tasks, time that an AI layer can return to risk judgment. BCG reports that 70% of insurers do not execute innovation because of IT limitations, which is exactly the constraint an additive layer removes, since it carries no load on the insurer's IT and requires no system migration. WIR is the AI layer for insurance. On top of the systems the insurer already runs, never in their place.
Where the automated journey compresses response time
The manual quotation journey leaks time at every handoff, and an automated journey closes each leak in sequence. The flow runs in six stages. First, multichannel intake with automatic validation captures submissions from email, broker portal, API, or upload into one structured queue, so no human triage has to start the clock. Second, intelligent document reading uses Machine Learning to extract structured fields from PDFs, inspection (vistoria) reports, asset schedules, and financial documents, which removes the slow re-keying step and the errors that bounce a case back later.
The middle of the journey is where senior underwriter time is usually consumed by lookups instead of judgment. Third, broker enrichment and context cross-references external and historical sources, CNPJ, broker history, exposure, and credit, then scores the submission so the underwriter sees a complete, comparable picture rather than chasing missing fields. Fourth, a risk and fraud engine, a multi-factor ML model calibrated to the insurer's risk appetite and underwriting manual, produces a risk score and probability and flags fraud signals, applying the same rules consistently across every case.
The closing stages turn analysis into a fast, recorded answer. Fifth, dynamic pricing calculates the risk-adjusted premium (prêmio) with the insurer's own rating logic, loadings, and discounts, with instant output. Sixth, decision and prioritization returns a quote, an automatic decline, or an escalation to a human, always with an explanation, writing back to the policy core and returning the audit trail. Straightforward risks move to instant quoting while genuinely complex ones route to senior underwriters with a visible SLA and an underwriter queue. The net effect lifts throughput without adding headcount, which matters in a market that, per company context, grows double digits per year while underwriting capacity does not keep pace.
How to deploy the external layer to speed up the response
Deployment is additive, so the insurer keeps its system of record and gains an intelligence layer over it. A practical sequence starts with scope. The insurer picks one or two high-volume lines, patrimonial or auto frota, where slow quote turnaround visibly costs broker business, and defines the response window it wants to make visible. Next comes integration with the existing core through API, portal, or upload, with the quoting and policy system staying the source of truth. Deployment is purely additive. The insurer keeps its system of record and gains an intelligence layer over it, with no core migration and no IT project its own team has to run.
Calibration is what makes the speed gain safe. The scoring and pricing models are tuned to the insurer's own underwriting manual and risk appetite, not a generic template, so faster never means off-appetite. The insurer then tests in parallel, running the automated journey alongside the manual one to compare decisions and turnaround and to tune the thresholds for auto-quote versus escalation. Go-live publishes the response window to brokers so the speed advantage converts into routed volume, and continuous operation monitors model performance, drift, and decision quality, recalibrating as appetite and loss experience change.
Timelines are concrete rather than open-ended. Setup runs 3 to 12 months and covers automations, integrations, tests, and go-live adjustments at a fixed price with KPIs agreed before the work starts. After go-live, continuous operation runs the platform in production under a billing model adjusted per client. For market context that frames the case for this kind of build, see WIR's insurance intelligence coverage of the Brazilian Seguros e Danos market.
Governance, explainability, and LGPD
Faster decisions cannot mean opaque decisions. Every automated quote, decline, or escalation carries the reasons and the data behind it, and the platform returns a complete audit trail. That trail is what lets underwriting, audit, and SUSEP supervision reconstruct why a decision was made, and it is also what lets the insurer trust the model enough to auto-quote in the first place. Human underwriters keep authority over escalated complex risks, so automation widens capacity without removing judgment from the cases that need it.
The data posture is built for Brazilian regulation. Insurance submissions carry personal and sometimes sensitive data, so the layer keeps data encrypted at every step, in transit and at rest, processes only what is necessary, and supports data-subject rights under the LGPD (Lei 13.709/2018). Automated decisions that affect a person engage the law's provisions on automated decision-making, including the right to request review, in line with ANPD guidance. Because the model encodes the insurer's own risk policy, an explainable automated journey supports SUSEP supervision rather than complicating it.
How WIR speeds up the response to brokers
WIR is an external AI layer that sits on top of the insurer's existing core, policy, and quoting systems and automates the quotation and underwriting journey according to the insurer's own risk-acceptance policy. It is 100% external, with no load on the insurer's IT and no core migration, and it is not an insurer, broker, or MGA, so it never carries risk. Its Machine Learning is calibrated to each insurer's risk appetite and underwriting manual, and every decision is explainable, returns a full audit trail, and is LGPD compliant with data encrypted at every step.
Two modules do the work the broker feels. Underwriter Intelligence automates the quotation journey per the insurer's risk policy, with real-time ML scoring calibrated to appetite, automatic routing by appetite and exposure, and predictive conversion analysis by product, risk, and broker, so underwriters spend their time on risk analysis and business development. Smart Sales adds distribution intelligence, mapping the portfolio by client and product, scoring upsell and next-best-action, and running multi-channel campaigns with an attribution trail. Real-time dashboards give a proactive view of in-flight deals and the pipeline, and the visible SLA gives the corretor a reliable answer to the question of when the quote arrives.
WIR's first traction is a POC in execution with a global insurer in the Transport line. The positioning holds across every engagement. WIR is the AI layer for insurance, on top of the systems the insurer already runs, never in their place, and the outcome it targets is the one the Capgemini figure points to. When the insurer answers faster and more consistently, it becomes the carrier brokers route to first.
Frequently asked questions
Why does response speed influence the broker's choice?
Response speed is a primary commercial lever because the broker controls distribution and shops the same risk to several carriers. According to Capgemini, 60%+ of brokers choose an insurer by response speed. The corretor routinely sends the same submission to multiple carriers, so the insurer that returns a clean, consistent cotação first earns a structural advantage on attention rather than price. A quote returned while the broker is still in-flow tends to convert.
Where does the manual journey lose the most time?
The manual journey leaks time at every handoff, most heavily in re-keying documents and in senior underwriters doing lookups instead of judgment. Deloitte finds underwriters spend 40% of their time on administrative tasks. An AI layer closes each leak in sequence, reading PDFs, inspection reports, and schedules automatically, then cross-referencing CNPJ, broker history, exposure, and credit, so the underwriter sees a complete, comparable picture rather than chasing missing fields.
Does the broker see the quote SLA?
Yes. The automated journey publishes a visible service level on the response window, so the broker knows when a quote arrives. When an insurer reliably answers within a stated window, more submissions route to it by default. Straightforward risks move to instant quoting while genuinely complex ones escalate to senior underwriters with a visible SLA and an underwriter queue. Real-time dashboards give a proactive view of in-flight deals and the pipeline.
Does speeding up the response require replacing the core?
No. WIR is an external AI layer on top of the insurer's existing core, policy, and quoting systems, never a replacement. Deployment is purely additive, with no core migration and no IT project the insurer's team has to run. BCG reports 70% of insurers do not execute innovation because of IT limitations, which is exactly the constraint an additive layer removes since it carries no load on the insurer's IT. Setup runs 3 to 12 months.
Does a faster response keep the decision explainable and auditable?
Yes. Faster decisions do not mean opaque ones. Every automated quote, decline, or escalation carries the reasons and the data behind it, and the platform returns a complete audit trail. That trail lets underwriting, audit, and SUSEP supervision reconstruct why a decision was made. The Machine Learning is calibrated to the insurer's risk appetite and underwriting manual, data is encrypted at every step and LGPD compliant, and human underwriters keep authority over escalated complex risks.