In Brazilian P&C (Seguros e Danos), the broker controls distribution and routinely shops the same risk to several carriers. The insurer that returns a clean, consistent quote first tends to win on attention, before price even enters the conversation. That makes quote turnaround time a commercial lever, not a back-office metric. According to Capgemini's World Insurance Report, more than 60% of brokers choose an insurer by response speed, so every hour a submission sits in a queue is a measurable disadvantage.
Where quote turnaround time is actually lost
The delay is rarely in the pricing calculation. It accumulates in the manual handoffs around it. A submission arrives by email or broker portal, waits for someone to triage it, gets re-keyed from PDFs and inspection reports, then stalls again while an underwriter chases missing fields and cross-references exposure and history by hand. Much of that friction is the unstructured-document problem: submissions land as PDFs, vistoria reports, and spreadsheets, and Gartner estimates corporate teams lose 20% to 30% of their time organizing unstructured data before they can act on it. On top of that, Deloitte finds underwriters spend roughly 40% of their time on administrative tasks, time pulled away from the risk judgment that actually needs their expertise. The clock the broker experiences is mostly the sum of these queues. Cutting turnaround means removing the handoffs, starting at submission intake, not asking underwriters to work faster.
How an external AI layer compresses the intake-to-quote path
An external AI layer sits on top of the systems the insurer already runs and automates the journey end to end, in six stages. Multichannel intake with automatic validation captures submissions from email, portal, API, or upload into one structured queue, so no human has to start the clock. Intelligent document reading uses Machine Learning to extract structured fields from PDFs, inspection (vistoria) reports, and asset schedules, which removes the slow re-keying step. Broker enrichment cross-references external and historical sources, CNPJ, broker history, exposure, and credit, then scores the submission so the underwriter sees a complete picture instead of chasing gaps. A risk and fraud engine, calibrated to the insurer's appetite and underwriting manual, returns a risk score and an automated recommendation. Dynamic pricing produces a risk-adjusted premium with instant output. The final stage returns a quote, an automatic decline, or an escalation to a human, always with an explanation and a visible SLA on the response window. That stated window compounds the gain, because when a broker knows an insurer reliably answers inside it, more submissions route there by default. Raising the share of cases that flow through without manual touch, the straight-through processing rate, is what compresses the path from days toward hours. This is the direction McKinsey describes in Insurance 2030: The impact of AI on the future of insurance, a future in which underwriting for many personal and small-commercial products becomes largely automated and decisions that once took days move toward near real time.
Faster quotes without giving up the core, or control
Speed does not require a core migration. The layer is external and additive, which matters because BCG finds 70% of insurers do not execute the innovation they want because of IT limitations. Rebuilding a policy or core system to chase turnaround puts the roadmap behind a multi-year program, so an approach that connects by API and asks nothing of the core usually delivers value sooner. Because the model is calibrated to the insurer's own risk appetite and underwriting manual, a faster answer is not a looser one. Every decision stays explainable and returns a full audit trail, data is encrypted at every step and handled under LGPD, and the SUSEP expectation that an acceptance or a decline can be justified is met by design. An insurer can add the AI layer without replacing the core system and keep the governance the regulator already requires.
How WIR reduces quote turnaround time
WIR is the AI layer for insurance. Its Underwriter Intelligence module automates the quotation journey per the insurer's risk-acceptance policy, with real-time scoring calibrated to appetite, automatic routing by appetite and exposure, and a visible underwriter queue, so the answer reaches the broker while the risk is still live. Smart Sales adds distribution intelligence on top, scoring next-best-action and upsell so the fastest response also reaches the highest-value submissions. WIR runs fully externally, with no load on the insurer's IT and no core migration, and its first traction is a proof-of-concept in execution with a global insurer in the Transport line. To see where your quoting journey loses time and how an external layer gives it back, book a conversation at wirinnovation.ai. WIR is the AI layer for insurance. On top of the systems the insurer already runs, never in their place.
Perguntas frequentes
How can insurers reduce quote turnaround time with AI?
By adding an external AI layer on top of the core they already run, rather than rebuilding it. The layer automates the submission-to-decision journey: multichannel intake, intelligent document reading, broker enrichment, risk scoring calibrated to appetite, and dynamic pricing. Removing the manual handoffs where cases queue is what compresses the path from days toward hours, so the broker gets a clean answer while the risk is still live.
Where do insurers lose the most time in the quoting journey?
In the manual handoffs around the decision, not in the pricing itself. Submissions wait for triage, get re-keyed from PDFs and inspection reports, and stall while underwriters chase missing fields. Deloitte puts about 40% of an underwriter's time on administrative work rather than risk analysis, so the turnaround a broker feels is mostly the sum of those queues. Automating intake and reading removes them first.
Does reducing quote turnaround time require replacing the core system?
No. An external AI layer connects by API and sits on top of the policy and core systems the insurer already runs, so there is no migration. This matters because BCG finds 70% of insurers do not execute the innovation they want because of IT limitations. An additive layer sidesteps that constraint and usually delivers faster quotes sooner than a multi-year core rebuild would.
How does faster quoting stay auditable under SUSEP and LGPD?
The model is calibrated to the insurer's own risk appetite and underwriting manual, so a faster answer follows the same acceptance policy a human would. Every quote, decline, or escalation returns an explanation and a full audit trail, which meets the SUSEP expectation that a decision can be justified. Data is encrypted at every step and handled under LGPD, so speed does not come at the cost of governance.
What is a good straight-through processing (STP) rate for faster quotes?
There is no single universal benchmark. A good STP rate is the highest share of submissions your risk appetite lets you decide without manual touch for a given line of business. The point is not to hit a fixed number but to raise that share safely with automation calibrated to your underwriting manual, since a higher STP rate is what pulls average quote turnaround time down.