Accelerating claims and underwriting while reducing leakage through governed AI workflows

Industry overview

Insurance underwriting and claims triage functions operate in high-volume environments where decisions must balance speed, accuracy, and regulatory compliance. Income statements, financial documents, risk disclosures, and claim files contain critical signals. But extracting and interpreting them manually introduces variability and delay.

The problem

The existing process relied heavily on manual effort:

• Underwriters manually parsing PDFs (ITR, bank statements, financial documents)
• Claims triage decisions based on human review and subjective interpretation
• Risk scoring calculated separately using spreadsheets or scripts
• Limited traceability for decision logic and document interpretation

This resulted in high turnaround time, errors in risk computation, operational inefficiency, exposure to leakage.

Our role

Ganit was engaged to design AI copilots that assist underwriters and claims teams while maintaining governance, explainability, and integration with core systems.

Methodology

Intelligent Document Extraction - Using GaniParser and OCR/IDP capabilities, we automated extraction of structured data from income tax returns, bank statements, claim documents, and supporting records.

Risk Scoring Automation - Extracted financial variables were fed into risk scoring models to compute standardized risk profiles. The scoring logic was embedded into the workflow to eliminate manual spreadsheet-based calculations.

Claims Triage Copilot - An AI-assisted triage layer evaluated incoming claims, applied rule-based validation, and prioritized cases based on risk signals. Low-risk claims moved faster through straight-through processing, while higher-risk cases were routed for deeper review.

Governed Workflow & Traceability - Every automated decision was backed by rule logic, computed metrics, and documented reasoning. Maker–checker workflows ensured oversight, while integration APIs connected outputs to core underwriting and claims systems.

How did we enable consumption?

• • AI-assisted recommendations within existing systems
• • Automated computation embedded directly in decision screens
• • Exception routing for high-risk or incomplete cases
• • Clear reasoning trails attached to every underwriting and triage outcome

Our impact

The transformation augmented underwriters.

• • Manual document parsing was replaced with reliable, structured extraction
• • Risk scores became standardized and reproducible
• • Claims were prioritized intelligently instead of sequentially
• • Decision trails became transparent and audit-ready

By embedding governed AI workflows into underwriting and claims triage, Ganit enabled faster processing, reduced leakage, and improved risk discipline without compromising regulatory control or operational oversight.