MOTOR & AUTO CLAIMS · P&C INSURANCE

Your claims run on invisible decisions.
Make them autonomous.

Every motor claim involves 100+ micro-decisions: liability splits, coverage checks, parts approvals, settlement amounts. Today they happen in emails and phone calls with zero audit trail. Autonomous operations make every one visible, consistent, and instant.

Book an Agentic Viability Session →See why the old model breaks ↓
Partnered with global leaders including:
Partnered with global leaders
90%of motor FNOL still arrives by phone callIndustry average
56%invoice validation checks fail on first passLeading EU insurer
2,100+FTE in a single carrier's claims back officeTop 10 EU insurer
70%of document review time remains manualIndustry benchmark
THE REALITY TODAY

The process looks digital. The work is still manual.

Behind every self-service portal and workflow tool, humans still touch every claim at multiple points. These are the pains that compound at scale.

Headcount scales linearly with volume

Every new claim requires human labor at intake, classification, coverage check, and settlement. There is no operating leverage. Growth requires hiring.

600K+ emails, classified by hand

Staff manually search for reference numbers, classify 100+ document types, and attach to the correct case. Quality erodes under speed pressure.

Every automation project rebuilds from zero

OCR vendor, rules engine, workflow tool, RPA bridge: four contracts and four integrations for every use case. Months go to plumbing, not value.

LLM approaches have not delivered

Generic AI lacks domain precision. Coverage checks require deterministic logic, not probabilistic guesses. Teams struggle to identify where AI adds value versus risk.

Each geography is a separate problem

Germany is phone-heavy, France is 100% email B2B, Italy integrates with the green card office, UK uses solicitors. One-size-fits-all automation does not work.

Workforce knowledge is walking out the door

Claims expertise lives in human heads: tribal knowledge, judgment calls never codified. Projected 50% retirement of claims professionals by 2028.

The Custom Death Stack

15 to 20 integrations duct-taped together. Each new change means touching 8 systems. It is not a technology problem. It is a structural trap.

Manual reconciliationbreaks monthly
RPA bridge scriptsbrittle
Workflow routing toolroutes, not decides
Rules enginecan't read documents
OCR vendorextracts, not understands

Each layer was added to solve the layer below it. None of them do the actual work.

The question isn’t “how do we optimize it?”
It's “why are humans still doing the work?”

Autonomous operations for motor claims. Not better automation of the old process. A new operating model where specialized agents decide and act end-to-end.

01

Policy wording becomes executable logic

Your motor policy endorsements, exclusions, territorial limits, and deductible schedules become the code. No developer translation. No rule engine maintenance. The Proof Agent reads the document and executes the coverage decision.

Already handles reinsurance treaty wording: documents far more complex than motor claims policies.
02

Every micro-decision: visible, auditable, instant

Liability split, repair authorization, OEM vs. aftermarket parts, rental duration, salvage value: every determination is logged and traceable. The four-eye principle shifts from blanket dual-review to exception-only oversight.

100+ decisions per claim. Zero audit trail today. Full transparency with autonomous operations.
03

Specialized agents, not general AI

RAG systems fail for coverage checks. You cannot point general AI at a policy document and expect it to handle subrogation waiver clauses or territorial exclusions. Each agent is purpose-built for its domain, combining neural understanding with symbolic reasoning.

04

Autonomy + control + an evolving platform

You need all three simultaneously. Miss any one and you get a science project. The platform handles new claim types (EV battery damage, autonomous vehicle liability) without custom engineering. Regulations change, the architecture adapts.

The R&D gap is 30+ months. Internal teams building motor claims AI face years of data collection, model tuning, and regulatory validation.

11 specialized agents. 6 process phases. Production-ready.

Every phase of motor claims, from first notice of loss to subrogation recovery, is handled by purpose-built agents. This is architecture, not a roadmap.

FNOL Intake Agent

Omnichannel capture: app, web, phone, email. Structures unstructured input across all motor sub-types and languages.

Document Processing Agent

Processes accident statements, police reports, photos, invoices. 100+ document types. Multilingual.

Coverage / Proof Agent

Policy wording as executable logic. Determines coverage, exclusions, and territorial limits — deterministically.

Fraud Detection Agent

Pattern analysis against fraud indicators. Cross-references claimant history, repair shop patterns, and timing.

Liability Agent

Analyzes accident circumstances under comparative/contributory negligence frameworks. Multi-vehicle and cross-border.

Damage Assessment Agent

Vehicle damage photos to component identification, severity estimation, and repair vs. total-loss determination.

Estimation Agent

Interfaces with Audatex/DAT (EU) and CCC/Mitchell (US). Parts sourcing and labor-rate validation.

Total Loss Agent

Actual cash value, comparable vehicles, salvage valuation, and owner-retention options.

Settlement Agent

Payment workflows, authority rules, reserve adjustments, and regulatory timing (US + Solvency II).

Rental & Repair Agent

Replacement-vehicle authorization, duration monitoring, repair-timeline alignment, and cost control.

Subrogation & Recovery Agent

Recovery opportunities, demand letters, cross-border coordination, and portfolio-level optimization.

THE OPERATING MODEL

Not a feature upgrade. A different operating model.

Before Otera
  • Humans touch every claim at multiple points
  • Coverage interpreted manually from policy docs
  • Decisions in emails and calls, no audit trail
  • Each geography requires separate process design
  • Four-eye review on every settlement
  • New regulations: months of changes across 8+ tools
  • Headcount grows linearly with claim volume
With Otera
  • Agents process end-to-end; humans oversee exceptions
  • Policy wording IS the code. Zero interpretation gap.
  • Every micro-decision logged, traceable, auditable
  • Jurisdiction-agnostic agents: same platform, local rules
  • Exception-only review. Provably correct = no second eyes.
  • Platform adapts. New regulations, same architecture.
  • Volume doubles, headcount does not.
Motor claims
THE NEW REALITY

What autonomous motor claims looks like

These are not projections. This is what becomes possible when specialized agents handle end-to-end processing and humans govern the exceptions.

12 wk
To Production

Not a pilot. Not a POC. Live autonomous operations processing real claims.

100+
Pre-built Agents

Templates across insurance, finance, and operations. Composable for multinational deployments.

0
Core System Changes

Sits on top of Guidewire, Duck Creek, SAP, or legacy. API, database, or RPA bridge. Your stack stays.

100+
Languages

German FNOL, French email B2B, Italian green card, UK solicitor claims. Same platform, every language.

100%
Decision Auditability

Every micro-decision traceable to source documents. Regulator-ready governance from day one.

8-fig
P&L Impact at Scale

Already proven at the world's largest agentic automation deployment. Operating-model transformation, not incremental savings.

Every enterprise asks these questions

“Multi-layer AI sounds expensive at our volume.”
It sounds expensive because in the old model, each layer is a separate vendor with separate integration cost. OCR vendor, rules engine, workflow tool, RPA bridge: four contracts, four integrations, four maintenance cycles. When the layers are native to a single platform, the cost equation inverts. The real expense is the 30+ months of internal R&D and the compounding maintenance of a custom stack that gets more fragile with every addition.
“Agentic AI feels less stable than deterministic rules.”
Correct, if you mean generic AI. General-purpose LLMs are not stable enough for coverage adjudication. Otera uses specialized agents that combine neural understanding (reading unstructured documents) with symbolic reasoning (applying policy logic deterministically). Coverage checks are provably correct against the source document, with full audit trails. The instability risk is in generic AI, not in domain-specialized agents.
“We bought a platform once. Phase 2 never happened.”
Phase 2 never happens when a vendor sells workflow and calls it transformation. Otera delivers three components simultaneously from day one: autonomy in execution, control over what the system does, and a platform that evolves. Not a phased roadmap. Production in 12 weeks. If one of those three is missing at go-live, it is a failed deployment.
“Can this integrate with our 30-year-old claims platform?”
Yes. Three integration approaches: direct API where available, database-level connectivity for core systems, and RPA bridges for legacy systems without modern interfaces. Already running on IBM mainframes and 30+ year old systems at production scale. Guidewire, Duck Creek, SAP, homegrown: the deployment adapts to your stack.
“Our operations differ by country. One solution won't work.”
Each entity maintains unique processes while sharing common automation components. Agents are language-agnostic (100+ languages), jurisdiction-aware. German phone intake, French email B2B, Italian green card, UK solicitor-driven: same platform, local configuration. Composable architecture designed for multinational complexity.
“How does voice/phone intake work?”
The FNOL Intake Agent handles omnichannel input: app, web, phone, and email. Voice is an input channel, not a separate product. Integration with existing contact center platforms (Oracle, Genesys) means no rip-and-replace of telephony infrastructure.
“We are evaluating multiple vendors. Why Otera?”
Other vendors in this space offer point solutions: document extraction, workflow automation, or conversational AI for intake. Each solves a fragment. Otera delivers autonomous operations: end-to-end processing where agents decide and act across the full claims lifecycle. The comparison is not feature-to-feature. It is automation versus autonomy. That is a different category, and it is why the world's largest agentic automation deployment chose this architecture.
YOUR PERSPECTIVE

Three lenses, one platform

Whether you are evaluating the strategic case, planning operational rollout, or assessing technical architecture.

Executive

CDO / COO / Head of Claims

Evaluating operating model transformation

  • Volume doubles without proportional headcount growth
  • Operating model: cost center to competitive advantage
  • 12 weeks to production. Not a multi-year program.
  • Full governance and auditability from day one
  • Running at the world's largest agentic deployment
Operations

Claims Ops / Transformation Lead

Planning operational deployment

  • 11 agents across all 6 motor claims phases
  • Glass, collision, theft, fleet, BI, cross-border
  • Business-in-the-Loop: your team governs the rules
  • Exception-only review replaces blanket dual sign-off
  • Works alongside your existing team during transition
Technical

Solutions Architect / IT Lead

Assessing integration and architecture

  • Sits on top. No core system changes. API-first.
  • Neural + symbolic: not a generic LLM wrapper
  • Decision-path traceability for every determination
  • SOC 2, ISO 27001, GDPR. European data residency.
  • Stateless architecture: zero data stored on Otera side
Independent recognition — zero paid analyst relationships, all mentions organic

Stop optimizing the old model.
See what autonomous looks like.

An Agentic Viability Session maps your motor claims process against autonomous operations architecture, identifies the highest-impact starting point, and shows the path to production in 12 weeks. 45 minutes. No pitch deck. Bring your process, we bring the architecture.