Customer story

Global Multi-line insurance providers 3x faster settlement

Deployed AI-powered claims automation - digitizing submissions, accelerating adjudication, and updating systems of record globally with regulatory compliance.

Industry

Insurance

region

Global

use cases

Agentic AI Claims Processing

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The Impact
3
x
faster settlement for low-complexity claims
99
%
accuracy in data extraction
40
%
reduction in Loss Adjustment Expenses through automation

The Challenge

The insurer’s multi-line claims operations - spanning health, auto, property, and specialty - were constrained by manual, document-heavy workflows. Provider invoices, medical records, repair bills, and police reports arrived as PDFs or scans, forcing handlers to manually rekey data into fragmented systems of record. This drove up loss adjustment expenses (LAE), extended cycle times, and created compliance risks, while limiting scalability during surge events.

The Solution

Unframe deployed an AI-powered claims automation solution that digitizes and validates unstructured submissions, auto-updates systems of record, and applies AI-based fraud and compliance checks. Routine, low-complexity claims flow through straight-through processing (STP), while exceptions are flagged for adjuster review. The carrier achieved faster adjudication, reduced leakage, stronger regulatory compliance, and improved policyholder satisfaction across global lines of business.

FNOL Intake → Claim Abstraction → Liability Reconcile → Agents Adjudicate → Exception Escalation → Audit Trail

As tailored as build & as fast as buy

Unframe gives you the best of both worlds - your data, your models, your governance - delivered through a platform that removes everything slowing you down. You stay in control. You just move faster.
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With AI-powered automation, our policyholders receive faster, reliable settlements, and we’ve significantly reduced LAE. Routine claims now flow straight-through, freeing our adjusters to focus on the complex cases where judgment really matters.

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