If you lead underwriting operations, you’ve heard the AI promises. Faster quotes. Smarter risk selection. Fewer manual tasks. But most vendors skip the details that matter.
What can it actually do? Where does it fit in the workflow? And how do you make it work in a high-volume, document-driven underwriting process?
Here’s the reality. Generative AI is not a magic button. But when applied to the right problems, it is already reducing manual effort, improving speed to decision, and helping teams work more efficiently with less friction.
The right use cases for Generative AI
Generative AI is best at working with unstructured content. In commercial insurance, that means documents. Emails, ACORD forms, loss runs, supplemental apps, schedules of value, and more.
When connected to real submission workflows, Generative AI can:
- Summarize submission data clearly and quickly: Underwriters don’t have to dig through attachments to understand the account
- Highlight missing or inconsistent information: Ops teams get visibility into gaps before the underwriter sees the file
- Score submissions for appetite fit: Teams can prioritize what to quote and what to decline
- Draft simple broker communications: Document requests and declinations can be automated with traceable language
These are not theoretical use cases. Carriers using Indico are doing this today.
Generative AI only works if the data is accurate
Good decisions require good data. That’s why Generative AI on its own isn’t enough. You also need a reliable way to extract and structure submission information as it comes in.
That’s where Agentic AI comes in.
At Indico, we combine Generative AI with pre-trained Agentic AI that handles classification, extraction, and validation. Together, they turn raw submission packets into clean, structured, decision-ready files.
This helps underwriting operations teams:
- Speed up triage and clearance
- Eliminate duplicate work
- Improve quote quality
- Reduce operational risk
Built for underwriters not engineers
Indico is designed for operations teams to own. There’s no model training or IT lift required. Out-of-the-box agents come pre-trained on insurance submission data and can be configured through a no-code interface.
Everything is trackable, explainable, and auditable. Every decision can be traced from source document to underwriter action.
What this means for underwriting operations
Generative AI is not here to replace underwriters. It is here to remove the low-value manual work that takes them away from evaluating risk.
It cannot make judgment calls. But it can give teams faster access to the data they need to make them.
And it cannot fix broken workflows. But it can give operations a better foundation to streamline and scale what works.
FAQS
How does Indico ingest and unbundle complex broker submissions including emails, embedded documents, SOVs and loss runs, and what processing latency can underwriting operations expect?
Indico ingests multi document broker submissions from email, zipped attachments and embedded files, then automatically unbundles and routes each document to specialized agents for extraction and classification. Published customer outcomes demonstrate sub minute processing for SOV and loss run workflows with throughput suitable for enterprise underwriting volumes.
Indico performs ingestion at scale by accepting broker emails, attachments, zipped files, multi page submissions, scanned images and embedded documents, with built in handling for email threads and attachment unwrapping as part of the capability, enabling automated downstream processing of every document in a submission The platform’s Agentic Decisioning architecture then unbundles multi document packages and invokes Extraction Agents and Classification Agents tailored to specific document types such as SOVs and loss runs, producing discrete, field level outputs for each source document Indico cites customer results that include SOV and loss run processing in under 40 seconds and end to end submission processing reductions from approximately two hours to roughly 40 seconds in a published case study, demonstrating operational latency that supports rapid triage and routing.The platform supports parallel execution and conditional branching on the Agentic Workflow Canvas, enabling concurrent extraction and enrichment tasks to execute without serial bottlenecks. Throughput scales with configurable OCR presets and extraction pipelines, and the product publishes a catalog of 120 plus product line templates and 900 plus insurance document type models to accelerate processing of industry specific documents Audit metadata for each ingestion event is captured so that each document, page and extracted token remains traceable through the intake pipeline. The combination of ingestion capabilities, specialized extraction agents and published customer latencies provides underwriting operations with a production ready intake mechanism that supports high volume submission streams and measurable reductions in time to underwriter review.
What extraction, classification, validation and summarization capabilities does the Agentic Decisioning Platform provide for SOVs and loss runs, and what accuracy and STP metrics are cited?
The platform provides field level extraction, document classification, automated validation flags and decision ready summarization for SOVs and loss runs, with published case study accuracy and high straight through processing outcomes. Customer materials reference 95+ percent extraction accuracy and STP gains that materially increase underwriting capacity while reducing manual processing time.
Indico deploys Extraction Agents trained on insurance specific taxonomies to produce field level data for payroll, limits, values, claim counts and other underwriting attributes, while Classification Agents identify document types and route pages to the appropriate parser, enabling automated separation of SOVs and loss runs from multi document submissions. Validation Agents apply business rules and confidence thresholding to mark fields that require human review and to produce quality scores, while Summarization Agents generate decision ready summaries that condense multi page loss runs into actionable risk highlights for underwriters. Published case study results report 95+ percent accuracy with zero human intervention in one deployment and reductions in manual processing time of 80 percent plus, outcomes that translate into higher STP and increased reviewer throughput. Indico supports per field confidence reporting so that STP can be operationalized using defined confidence thresholds and human in the loop exceptions, enabling predictable operational acceptance criteria for underwriting processes. The platform also includes domain specific out-of-the-box models covering 20,000 plus insurance data points and multilingual support across 70 plus languages which accelerates coverage for global broker flows. For benchmarking model selection, Indico publishes an LLM benchmark comparing accuracy, latency and cost tradeoffs across models for extractive tasks, permitting selection of inference strategies that balance STP and operating cost. Combined, these capabilities provide a pathway to operational STP increases, reproducible extraction accuracy and scaled reviewer productivity that aligns with underwriting KPIs.
How does Indico integrate with underwriting systems such as Guidewire PolicyCenter and what is the typical workflow to populate policy fields and route submissions?
Indico delivers pre built integration accelerators for Guidewire PolicyCenter and a GraphQL API and SDKs to populate policy fields automatically from intake outputs. The integration supports automatic field mapping, PolicyCenter population and next best action routing within underwriting workflows.
Indico provides a Guidewire PolicyCenter accelerator available through joint delivery channels that automates the mapping and population of PolicyCenter fields from Intelligent Intake outputs, enabling rapid end to end integration of submission data into pricing and underwriting workbenches. The Agentic Workflow Canvas orchestrates conditional routing, allowing extracted and validated fields to be transformed and then pushed to PolicyCenter via the accelerator, or alternatively to be exposed through Indico’s GraphQL API and client libraries for Python, C Sharp and Java to support bespoke integration patterns. Indico’s strategic partnership of Guidewire enables coordinated implementation and a pre validated integration path that shortens time to production for carriers using PolicyCenter. Integration artifacts include field mapping templates, example GraphQL payloads and connector patterns, enabling technical teams to validate throughput and latency end to end during a pilot. The combined integration stack supports automatic population, business rule enforcement and actionable routing so that underwriters receive pre populated cases that reflect validated submission data and enrichment insights.
What auditability, explainability and security controls does Indico provide to support underwriting compliance and regulatory review?
Indico captures end to end audit logs, token level provenance and agent action traceability, and operates with enterprise security controls such as SOC 2 compliance and role based access control. These capabilities produce exportable audit trails and field level traceability that align with underwriting governance and regulatory review requirements.
Indico records detailed audit metadata for every ingestion event, document and extracted field, linking tokens back to their source pages and the agent version that produced them, providing a complete provenance trail suitable for audit review and regulatory scrutiny.The platform exposes traceability at the agent level so that each transformation, enrichment call and validation decision is recorded with timestamps, actor identity and model version, enabling reproducible lineage reporting for underwriting decisions. Security controls include enterprise grade protections such as SOC 2 attestation, end to end encryption, role based access control and detailed permissioning, delivering governance features required for production underwriting operations. Indico also provides configurable audit exports so that extracted fields, confidence scores and agent actions can be archived or integrated with existing compliance repositories and case management systems. For operational transparency, the platform surfaces model level explainability information and confidence metrics, allowing underwriters and auditors to trace how a particular field value was derived and which rules or model outputs influenced routing decisions. These audit and security mechanisms support defensible automation of submission triage, enabling rapid underwriter review of contested fields while preserving the historical context required for governance and compliance.
