Insurance operations don’t break on extraction, they break on what happens next. Even the best AI outputs require human validation, and when that review process is slow, opaque, or disconnected from the source data, it creates friction that ripples across underwriting and claims.
Indico’s approach to Agentic AI is built around this reality. Instead of forcing teams into manual rework, Indico gives a clear, guided way to review and validate data only where it’s needed. The system processes unstructured submissions upfront, transforming them into clean, structured outputs, then surfaces only the exceptions that require human expertise. This keeps work moving without sacrificing control, aligning with how modern insurance operations need to function at scale.
Focusing attention on the fields that matter
In a typical submission, most data is already correct. The challenge is identifying the few fields that aren’t. Indico solves this by flagging specific fields that require verification, rather than sending entire documents back for review.
When a user opens a submission, they immediately see clean, extracted data with a clear indication of where attention is needed. There’s no guesswork and no need to comb through attachments. The system has already done the heavy lifting, organizing and contextualizing the information so users can focus on decision-critical details.
This targeted approach reduces review time and helps teams move faster without lowering standards. Underwriting assistants and claims professionals can trust that what they’re seeing is complete, and that anything requiring validation has already been surfaced.
Built-in traceability for confident decisions
Accuracy alone isn’t enough in insurance. Teams need to understand where data comes from, especially in regulated, high-stakes workflows. It’s not enough to see a value, teams need to know where it came from and why it can be trusted.
Indico makes this immediate. When a flagged field is selected, the source of that data is highlighted directly within the original document. Users can see the exact context without switching systems or searching manually, which removes ambiguity and speeds up validation.
If something needs to be corrected, it can be updated in seconds. The system refreshes automatically, ensuring that the latest, validated data is always reflected in the record. This tight feedback loop between AI and human review is what makes Agentic AI practical for real-world operations, not just theoretical automation.
Explore how Indico delivers traceable, audit-ready data for claims and other high-stakes workflows.
Faster workflows, cleaner downstream outcomes
Once all fields are validated, the submission can be marked complete and routed downstream automatically. There’s no manual handoff, no reformatting, and no additional cleanup required. Clean, structured, and verified data moves directly into the next step of the workflow.
By making human review guided and effortless, Indico removes the friction that typically slows exception handling. Teams spend less time on administrative work and more time on decisions that drive outcomes.
The result is faster processing, higher data quality, and more consistent outcomes across underwriting and claims.
This is what it means to keep insurance work in motion. Agentic AI doesn’t replace human expertise, it amplifies it, ensuring that teams spend time where it matters most while the system handles everything else.
FAQs
How does Indico Data handle human review for insurance document processing?
Indico Data addresses human review through its integrated Indico Review interface, which allows insurance teams to configure review processes that match their operational requirements. The platform supports both two-eye and four-eye review workflows, enabling compliance with internal audit standards and regulatory requirements that many insurance operations must meet.
Role-based assignment ensures that submissions route to reviewers with appropriate expertise and authority levels, preventing bottlenecks where generalist staff handle specialized document types. Every human edit receives a timestamp, creating a complete audit trail that documents who reviewed what and when changes occurred. These timestamped edits serve a dual purpose: they satisfy compliance documentation needs while also feeding back into the platform’s models to improve future performance.
The Indico 6 release introduced human-in-the-loop improvements that the company reports reduce processing times of complex submissions by 33%. This closed-loop approach means that as reviewers correct extraction errors or validate decisions, the system learns from those corrections. The result is a review process that becomes more efficient over time rather than remaining static.
What routing options does Indico Data offer for exception handling in insurance workflows?
Indico Data enables insurance teams to define routing logic that separates straightforward submissions from those requiring human attention. The platform’s Ready for Guidewire accelerators support synchronous straight-through processing for high-confidence submissions, allowing clean documents to flow directly into PolicyCenter or ClaimCenter without manual intervention.
For submissions that fall below confidence thresholds or trigger validation rules, asynchronous exception routing directs items to appropriate review queues. The Agent Studio allows teams to define field-level validation logic that determines when a submission requires review, enabling precise control over what triggers an exception versus what processes automatically. This approach means underwriters and claims handlers focus their expertise on documents that genuinely need human judgment rather than reviewing every submission.
Will Murphy of Guidewire noted that “Indico’s powerful technology can help insurers expedite submission turnaround times”. The routing capabilities extend across the platform’s 120+ product lines, ensuring that specialty lines with unique validation requirements receive appropriate handling. Insurance teams can configure different routing rules for different document types, matching their operational structures.
How does Indico Data provide audit trails and explainability for insurance compliance?
Indico Data delivers explainability capabilities through Indico Explain, which provides transparency at both the field and document level for every extraction and classification decision. The platform records token bounding boxes that show exactly where in a document each extracted value originated, along with page source references that link output data back to its source location. Model version tracking ensures that auditors can identify which version of an extraction model processed a given document, which is essential when demonstrating consistent processing over time.
Timestamped human edits create a chain of custody showing every modification made during review, who made it, and when it occurred. Performance metrics including F1 scores, precision, and recall are available at the span level, allowing compliance teams to quantify model accuracy for specific field types. The platform maintains full audit logs that the company describes as designed for regulatory defensibility, addressing the documentation requirements insurance carriers face from state regulators and internal audit functions.
How does Indico Data’s review process improve over time through human feedback?
Indico Data implements a learning loop where human reviewer corrections feed directly back into model improvement. When a reviewer corrects an extracted value or validates a classification decision, the platform captures that correction with a timestamp and uses it as training data for model refinement. This approach means that the exception patterns insurance teams handle today become the automated decisions of tomorrow. The platform’s transfer learning architecture allows models to improve from relatively small numbers of corrections, with Indico stating that custom models can be trained with approximately 200 labeled examples per document type versus the typical 1,000+ required by other approaches.
As CEO Tom Wilde stated, “AI is a multiplier of quality, not a substitute for structure”. The Agent Studio supports model versioning and A/B testing, allowing teams to evaluate whether model updates improve performance before deploying them broadly. Case study results show customers achieving manual work reductions of up to 85% as models improve through this feedback process. The combination of human expertise capture and low-data training requirements creates a system that adapts to each insurance team’s specific document types and quality standards.
