To reduce administrative tasks in insurance underwriting with AI means adding an external AI layer on top of the systems the insurer already runs, so the work that surrounds the risk decision is handled by software instead of by the underwriter. The layer sorts incoming submissions, reads documents, validates and enriches data, routes each risk, and drafts the decision rationale, then hands a clean, scored file to the right person. The underwriter (subscritor) stops re-keying and chasing paperwork and spends the recovered hours on risk analysis and on winning business.
This is an operating-model question, not a software-shopping one. The reader here is usually a head of underwriting, a subscrição lead, or a C-level focused on capacity and productivity, and the constraint is real. Underwriters spend around 40% of their time on administrative tasks (Deloitte), which is qualified judgment spent on work that needs no judgment. Reducing that load is how an insurer scales premium without scaling headcount, and it does not require touching the core. WIR is the AI layer for insurance in Brazil. On top of the systems the insurer already runs, never in their place, it absorbs the administrative envelope around the underwriting decision in the Seguros e Danos (P&C) market.
How the AI layer absorbs administrative tasks end to end
The layer takes over the administrative work along the same six-stage flow the underwriting journey already follows, so it is clear what leaves the underwriter's desk and what stays. The goal at each stage is the same: do the non-judgment work at machine speed and surface only what needs a human.
Stage one is multichannel intake with automatic validation. The layer ingests submissions from API, broker portal, and upload of e-mail, PDF, and spreadsheet, registers each with an ID, timestamp, and source broker, starts the clock, and runs a mandatory-field check. When data is missing, it issues an automated request to the corretor (broker) instead of a person chasing it. Stage two is intelligent document reading, where extraction models read propostas, schedules, and financials and normalize the fields into the insurer's data dictionary. This removes the single biggest time sink, the manual re-keying, since corporate teams lose 20% to 30% of time organizing unstructured data (Gartner). Stage three is broker enrichment and context. The layer validates the CNPJ, pulls prior policy and claims history, and assesses portfolio quality and exposure, so the underwriter receives an enriched file rather than opening five tabs.
Stage four is the risk and fraud ML engine, a multi-factor model that scores each risk against the insurer's own loss data and risk appetite and flags anomalies. It clears the obvious cases and escalates the ones that need judgment. Stage five is dynamic pricing, a risk-adjusted premium calculated inside the bands the team sets and returned instantly. Stage six is decision and prioritization: each risk is returned as a quote, an automatic decline on a knockout rule, or an escalation, each with a drafted rationale and an audit trail, ranked by exposure, win probability, and time against SLA. The judgment on complex risks, the appetite calls, and the corretor relationship stay with the underwriter.
How to deploy the external AI layer to reduce administrative work
A staged rollout removes administrative work progressively, keeps the core untouched, and proves the time recovered before widening scope. With WIR, setup runs as a one-time engagement of 3 to 12 months, with a fixed price, a clear scope, and KPIs agreed before the work starts, followed by continuous operation after go-live.
The first step is scope and baseline. The insurer picks one or two lines (ramos) and one channel to start, for example SME Patrimonial or Transportes cargo through the broker portal, and measures the current state first: hours spent on intake, re-keying, lookups, and rationale-writing, plus quote turnaround and hit ratio. Without that baseline the productivity gain cannot be proven later. The second step is integration with the core, connecting intake and the write-back of structured results to the policy system of record. The core stays the system of record, and no migration of historical policies is required to begin.
The decisive step is calibration to the underwriting manual and risk appetite. The validation rules, enrichment sources, knockout rules, authority bands, and pricing bands are encoded from the insurer's own manual, and the models are tuned on the insurer's own loss history. This is what makes the automated output the insurer's output at machine speed, not a generic market default. After that comes testing in shadow mode, running the layer in parallel on live submissions to compare its extraction, scoring, and drafted rationales against what underwriters produce by hand. Then a staged go-live on the narrowest, cleanest band first, with conservative escalation. Continuous operation closes the loop, tracking underwriter hours returned, share of submissions handled without manual re-keying, quote turnaround, and routing accuracy, and feeding underwriter overrides back as a training signal.
Governance, explainability, and LGPD
Taking administrative tasks off the underwriter raises the governance bar rather than lowering it, because work a person used to do by hand is now done at volume by the layer. Every automated task and decision must be explainable and auditable, and in the Brazilian frame that is a requirement, not a convenience. This is also why bolting more software onto a legacy stack is hard: roughly 70% of insurers say IT limitations keep them from executing innovation (BCG), which is exactly the problem an external layer is built to avoid.
Explainability means each extracted field, each enrichment, each score, and each automatic quote, decline, or escalation carries the inputs and the rationale that produced it, so underwriters and auditors can reconstruct any automated step after the fact. The audit trail records every stage from intake to decision with timestamp, inputs, model version, and outcome. Under the LGPD (Lei Geral de Proteção de Dados, Lei 13.709 de 2018), insurance submissions carry personal and sometimes sensitive data, so the layer processes only what is necessary, on a lawful basis. Article 20 gives the data subject the right to request review of decisions taken solely on automated processing, which is precisely why the escalation-to-a-human path and the explainability of each step are compliance requirements.
With WIR, data is encrypted at every step, access is controlled and segregated, and the Machine Learning is calibrated to the insurer's appetite and manual, which is the mechanism that keeps automated output consistent with the insurer's stated policy. The insurer remains the risk owner and the decision authority on every escalated case. The layer does not promise an infallible outcome. It states the mechanism, returns the evidence, and leaves the judgment with the underwriter.
How WIR frees underwriters from administrative tasks
WIR is the AI layer for insurance, an external intelligence platform that sits on top of the insurer's existing core, policy admin, and rating systems and connects through integration, not replacement. It is 100% external, so there is no load on the insurer's IT and no core migration. WIR automates the quotation and underwriting journey according to the insurer's own risk-acceptance policy, and its Machine Learning is calibrated to the insurer's risk appetite and underwriting manual rather than to a generic benchmark. This is the difference between a tool and the insurer's own process expressed at machine speed.
Two modules carry the work. Underwriter Intelligence automates the quotation journey so underwriters analyze risk and focus on business development, with real-time ML scoring calibrated to appetite, automatic routing by appetite and exposure, and predictive conversion analysis by product, risk, and broker. Smart Sales adds distribution intelligence, mapping the portfolio by client and product, scoring upsell and next-best-action, and running multichannel campaigns with an attribution trail, so penetration and retention grow together. Real-time dashboards and analytics give a proactive view of in-flight deals and the pipeline. Across all of it, every decision is explainable and returns a full audit trail, and data is LGPD compliant and encrypted at every step. This matters because 60%+ of brokers choose an insurer by response speed (Capgemini), and removing the administrative load is what lets a quote come back fast enough to win the corretor.
WIR was born from accumulated operational experience and is built with Mahway, a Venture Builder in California, and Avante, a Venture Studio in Brazil. Its first public traction is a POC in execution with a global insurer in the Transport line. The honest framing is the product itself: the external AI layer absorbs the administrative scaffolding around the decision so the subscritor spends the recovered hours on risk analysis and on the relationships that grow the book.
Frequently asked questions
How much underwriter time is consumed by administrative tasks?
Underwriters spend around 40% of their time on administrative tasks (Deloitte). That is qualified judgment spent on work that needs none: sorting submissions, re-keying data from e-mail, PDF, and spreadsheet into the core, validating fields, pulling history, and drafting routine rationales. In a Seguros e Danos market growing double digits, this makes capacity, not appetite, the ceiling on growth. Reducing that load is how an insurer scales premium without scaling headcount.
Which administrative tasks does the AI layer absorb in underwriting?
The layer absorbs the non-judgment work around the decision. It sorts and registers incoming submissions and checks completeness, reads documents and extracts fields automatically so no one re-keys, validates the CNPJ and pulls prior policy and claims history, runs first-pass risk and fraud screening, calculates a risk-adjusted premium within set bands, and drafts the decision rationale with an audit trail. Complex, borderline, and high-exposure risks, plus the appetite calls and broker relationship, stay with the underwriter.
Does reducing administrative work mean replacing the insurer's core?
No. WIR is an external AI layer that sits on top of the insurer's existing core, policy admin, and rating systems and connects through integration, not replacement. It is 100% external, with no load on the insurer's IT and no core migration. The core stays the system of record for binding, issuance, and regulatory reporting. The layer absorbs the administrative envelope around the decision and writes structured results back to the core, so no historical-policy migration is required to begin.
How does the insurer measure the time underwriters recover for risk analysis?
By baselining before go-live, then tracking the shift in operation. Before turning anything on, the insurer measures hours spent on intake, re-keying, lookups, and rationale-writing, plus quote turnaround and hit ratio. After go-live it tracks underwriter hours returned, the share of submissions handled without manual re-keying, quote turnaround, SLA adherence, and routing accuracy. Real-time dashboards make the movement visible, so the recovered hours and the capacity gain can be shown to the board rather than assumed.
Do the automated tasks stay explainable and auditable?
Yes. Every extracted field, enrichment, score, and automatic quote, decline, or escalation carries the inputs and rationale that produced it, and every stage is logged with timestamp, inputs, model version, and outcome. Underwriters and auditors can reconstruct any automated step after the fact. Under the LGPD, Article 20 gives the data subject the right to request review of solely automated decisions, which is why escalation to a human and explainability are built in. Data is encrypted at every step and LGPD compliant.