FNOL process automation is the next opportunity for underwriting ops

Indico ingests multi‑format FNOL submissions, applies configurable OCR and extraction agents, and attaches token‑level provenance to every extracted field for auditability. This produces JSON outputs with source page, bounding boxes, model version, and human edit histories that enable traceable, auditable underwriting decisions.

Indico accepts emails, attachments, zipped bundles, native and scanned PDFs, TIFs, images, Excel files, ACORD forms, loss runs, SOVs, and handwritten fields, enabling a single intake pipeline for mixed FNOL inputs. The platform executes a staged Extraction Agent that performs OCR, preserves table and cell structure, and extracts tokens and bounding boxes for each field, yielding structured JSON output that separates component_results and model_results for downstream systems. Each extracted value is annotated with field‑level provenance metadata, including the source page index, token bounding coordinates, model version identifier, and timestamped human edit history, enabling reproducible audit trails and exportable logs for compliance reviews. Indico’s Enrichment Agents normalize values such as tenant occupancy, total insured value, and deductibles, and they link related emails and documents to create a consolidated FNOL record that underwriters can consume as decision‑ready data. Outputs are delivered as consumable JSON and can be surfaced through REST, GraphQL, or SDKs for real‑time integration with underwriting workflows, carrying provenance with each field for automated reconciliation and manual verification. The platform supports configurable OCR presets that include simple, standard, and detailed modes to tune latency and accuracy per document class, enabling operational tradeoffs to be governed by underwriting SLAs. Visualization of source images with highlighted bounding boxes and an editable review UI allow underwriters to validate or correct values, with every correction recorded against the originating model version, which supports forensic review and model improvement cycles. Case studies report extraction workflows that process SOVs and loss runs in under 30 seconds per document class in production deployments, demonstrating practical throughput and response time characteristics for high‑volume FNOL pipelines.

Indico provides ready‑for‑Guidewire accelerators that auto‑populate PolicyCenter and ClaimCenter with extracted FNOL fields, plus general connectors and SDKs for custom integrations. Integration is available via GraphQL, REST APIs, SDKs, and webhook/SNS notification patterns, enabling synchronous or asynchronous population of core systems.

Indico publishes Guidewire accelerators that map extracted FNOL and underwriting triage fields directly into PolicyCenter and ClaimCenter, including preconfigured field mappings and workflow hooks that accelerate go‑live timelines for claims and policy intake use cases. The platform exposes a REST API and a GraphQL sandbox for programmatic submission, result retrieval, and query of agent outputs, in addition to SDKs for Python, Java, and CSharp, which support ingestion patterns commonly used to integrate with legacy middleware and enterprise service buses. Indico supports push notification models using SNS and webhooks to notify downstream systems when processed FNOL payloads are available, enabling real‑time routing, auto‑assignment, and next‑best‑action triggers inside ClaimCenter or PolicyCenter. The accelerators include configuration for field mapping, error handling, and back‑sync behavior so that corrected or enriched values from human review are reflected back into the core system of record, preserving audit metadata for each update. Integration patterns support both synchronous population for high‑confidence STP flows and asynchronous case generation for human‑in‑the‑loop triage, enabling underwriter workload balancing and queue management within existing core workflows. Indico documents environment promotion features such as agent export/import and environment configuration to manage connector settings across dev, test, and production estates, which reduces deployment risk and accelerates iterative mapping refinement. Example case deployments show accelerated mapping and ingestion that reduced submission processing times from days to minutes, demonstrating practical efficacy of the accelerators in enterprise migrations.

Indico supports on‑premises, private‑cloud, and isolated deployments, and provides SOC 2 and HIPAA compliance, role‑based access, end‑to‑end encryption, and detailed audit logs. Every transaction includes exportable provenance and traceability to support regulatory reviews and internal governance.

Indico documents support for cloud, private‑cloud, and bare‑metal on‑premises deployments, enabling deployment topology choices that align with corporate data residency policies and underwriting data segregation needs. The platform implements end‑to‑end encryption, role‑based access control, and detailed audit logging with field‑level provenance that ties each extracted value to source documents, model versions, and human edits, which supports regulatory reporting and forensic analysis. Indico maintains compliance attestations including SOC 2 and HIPAA, and it offers architecture patterns to isolate processing from third‑party services when required for sensitive underwriting workflows. Audit exports include token‑level traceability and timestamped action logs that can be consumed by governance teams for audit, quality assurance, and supervisory reporting, enabling demonstrable accountability for every automated FNOL decision. The platform’s human‑in‑the‑loop tooling and Staggered Loop Training capture reviewer corrections and tie them to model versions, creating a documented improvement trail that aligns with model governance frameworks and internal change control processes. Indico’s security and compliance resources describe operational controls for incident response and role‑segregated access management, supporting enterprise procurement and legal review cycles. Production case studies reference rapid deployment into enterprise estates under governed controls, demonstrating that the security and deployment model supports scale and auditability in carrier environments.

Indico provides REST and GraphQL APIs, SDKs in Python, Java, and CSharp, webhook/SNS notification support, and developer tooling for submission, retrieval, and agent lifecycle management. The platform surfaces JSON outputs with component and model results, supports agent export/import for promotion, and produces measurable latency and accuracy metrics that can be instrumented for production monitoring.

Indico publishes a developer portal with REST endpoints, a GraphQL sandbox, and SDKs for Python, Java, and CSharp that enable programmatic submission of FNOL bundles, asynchronous retrieval of JSON results, and webhook/SNS patterns for notification on completion. The platform returns structured JSON with component_results and model_results sections, including confidence scores and field provenance so that downstream monitoring can compute STP rates and field‑level precision and recall in production. Agent lifecycle tooling supports export and import of agents for environment promotion, which enables CI/CD patterns for model releases and consistent metric collection across dev, test, and production environments. Operational metrics surfaced by the platform include per‑document latency, throughput, and confidence thresholds, which can be consumed by underwriting dashboards to track median and 95th percentile latencies, documents per second, and STP percentages by line of business. The platform supports configurable OCR modes to balance processing time and accuracy, enabling metric‑driven tuning for high‑volume FNOL channels such as emailed bundles or mobile photo uploads. For integration readiness the developer documentation provides sample SDK patterns for submission and result polling, webhook subscription examples, and JSON schema examples for mapping into PolicyCenter or ClaimCenter, which expedites instrumentation for throughput and quality monitoring. Case studies document real world throughput and latency examples such as SOV and loss‑run processing under 30 seconds in production, enabling benchmarking for pilot success criteria and operational SLAs. The developer tooling and exported metadata support automated analytics and reporting required by underwriting operations to measure FTE impact, time‑to‑first‑action, and STP gains.

Indico recommends a focused pilot that includes a representative sample of FNOL emails, attachments, ACORDs, loss runs, and SOVs, with clear KPIs such as field‑level precision/recall, STP%, median and 95th percentile latency, and underwriter time‑to‑first‑action. Pilot data should include realistic error cases, handwritten notes, and multi‑page bundles to exercise extraction, enrichment, routing, and Guidewire accelerator mappings.

A recommended 6 to 8 week pilot scope includes ingestion of a defined sample set of emails plus attachments, scanned ACORDs, loss runs, and SOVs representative of the carrier’s lines of business, with labeling of target fields such as insured name, policy number, date of loss, location, and total insured value to enable field‑level evaluation. Success metrics include field‑level precision, recall, and F1 for each core field, STP percentage (percent of FNOLs processed without human edit), median and 95th percentile latency per document type, and time‑to‑first‑action measured in minutes for routed claims, which align directly to underwriting KPIs such as time‑to‑quote and underwriter throughput. Indico’s accelerators and pre‑trained insurance agents reduce labeling requirements by leveraging existing models for ACORDs, loss runs, and SOVs, enabling faster pilot ramp up and measurable baseline performance within weeks. Pilot configuration should include Guidewire mapping validation sessions using the PolicyCenter/ClaimCenter accelerator so that field mappings, error handling, and back‑sync behaviours are tested under the pilot workload. Instrumentation during the pilot should capture provenance metadata, reviewer correction rates, model versioning, and drift indicators so that staged loop training can be applied to incrementally improve accuracy while preserving audit logs. Indico provides deployment and operational playbooks that describe environment promotion, agent export/import, and webhook/SNS configuration, which support repeatable validation and controlled rollout from pilot to production. Historical case outcomes provided by Indico, such as processing SOVs in under 30 seconds and reduction of manual processing from days to minutes, provide benchmark targets that underwriting operations can use to define go/no‑go thresholds and ROI calculations for the pilot.