AI-Powered Motor Claims
Automation for a Leading
Motor Insurance TPA in India.
Automation for a Leading
Motor Insurance TPA in India.
Computer vision and deep learning transform unstructured image-based motor claims into structured, explainable, decision-ready outputs — automating damage detection, scoring, and repair recommendations at scale.
Computer vision and deep learning transform unstructured image-based motor claims into structured, explainable, decision-ready outputs — automating damage detection, scoring, and repair recommendations at scale.
2
AI models deployed
13+
Vehicle parts detected
5
Damage types classified
Automated
Claim triaging
Damage assessment remains
manual and expertise-driven.
Motor insurance ecosystems — particularly in high-growth markets like India — face structural inefficiencies that scale with claim volume, not technology.
Jump to the solution →Motor insurance claims processing in India is dominated by manual adjudication. Image-based First Notice of Loss (FNOL) adoption is rising, but without standardisation — policyholders upload inconsistent photos, missing angles, and incomplete coverage, triggering repeated follow-ups and delays.
Adjudicators manually map visible damage to vehicle parts with no standardised classification framework. Outcomes vary by individual expertise. Operational costs increase linearly with claim volumes, creating an unsustainable model as digital FNOL scales.
The strategic gap is clear: while digital FNOL is common, AI-led damage intelligence is still underpenetrated. The client engaged Clarion Analytics to close that gap — transitioning from subjective manual review to automated, explainable, decision-ready outputs.
Part-level segmentation
on every image submitted.
Side-profile detection — 13 vehicle parts segmented simultaneously at confidence ≥ 0.6. Each part receives a unique colour overlay: Hood_Bonnet, Front_Bumper, Left_Fender, Left_Front_Door, Left_Rear_Door, Left_Quarter_Panel, Roof, tyres and glass panels all identified in a single pass.
Three systemic bottlenecks
that AI can eliminate.
Policyholders uploaded incomplete or inconsistent vehicle images — missing angles, poor lighting, partial coverage. Each gap triggered a repeat request, adding days to every claim cycle and creating a bottleneck before assessment even began.
Adjudicators manually mapped visible damage to vehicle parts with no standardised classification framework. Outcomes varied by individual expertise and attention, making consistency across claims impossible to guarantee or audit.
High dependency on skilled adjudicators meant that scaling claim volume required scaling headcount — a linear and expensive relationship. Rework from inconsistent inputs compounded costs further, with limited visibility into where inefficiencies originated.
From subjective adjudication
to structured intelligence.
Two models. Every part.
Every damage type.
The AI engine runs two parallel deep learning models — a Car Parts Detection model that segments granular vehicle components, and a Damage Detection model that identifies and classifies damage by type. Together they produce part-level damage mapping in a single processing pass.
A guided mobile stencil workflow ensures image quality and completeness before the AI engine runs — capturing hood, sides, rear and other angles in a predefined sequence to guarantee full vehicle coverage.
Hood_Bonnet, Roof and Grill detected with fine-grained segmentation boundaries per submitted image.
Front_Bumper and Rear_Bumper segmented independently — among the highest-frequency damage zones in motor claims.
Left and right front and rear doors, door glass, windshield and quarter panels — all detected and mapped individually.
Front and rear lamps, side mirrors — all flagged for lamp_broken damage type and structural integrity assessment.
Tyres and wheels segmented with count detection. Pillar and fender structural components mapped for severity scoring.
Five damage types classified per part: dent, scratch, crack, lamp_broken and missing_parts — each with a damage score and damage percentage.
From raw image
to structured claim intelligence.
Rear-view detection — 7 parts segmented on a damaged Maruti Swift at confidence ≥ 0.6. Diggi_Back_Door, Rear_Bumper, Grill, Left_Taillight, Right_Taillight, Right_Headlight and Back_Door_Glass all identified and bounded independently.
Adjudication dashboard output — part-level damage table with Part Name, Damage Type, Damage Score and Damage % per component. 4 unique damage types detected: missing_part, dent, lamp_broken, crack. Edge case flagged: 1 damage detected without a corresponding part detection.
End-to-end pipeline.
From FNOL to decision.
Seven stages from mobile image capture through to adjudicator review — each component purpose-built for motor claims at scale, with human-in-the-loop override at every decision point.
Measurable outcomes.
No asterisks.
The deployment transitions the client from manual, inconsistent, and slow adjudication to AI-assisted, scalable, and standardised claims processing — with a structured audit trail that manual review could never produce.
The dual-model AI engine processes every submitted claim image through part detection and damage classification in a single automated pass, producing structured outputs that require no manual transcription before adjudication begins.
Reduced dependency on large adjudication teams. Standardised inputs eliminated rework. Adjudicators shifted focus from manual inspection to exception handling — reviewing AI outputs rather than building them from scratch.
Every claim produces the same schema: Part Name, Damage Type, Damage Score, Damage %. Visual segmentation overlays provide adjudicators with explainable, auditable AI outputs — building operational trust in the system.
Foundation for
touchless claims processing.
This deployment is the first phase of a longer transition. The architecture, data schema, and feedback loops established here create the foundation for fully automated — eventually touchless — claims processing as model maturity and client confidence grow.
As model confidence grows, the human-in-the-loop layer progressively reduces — moving toward fully automated adjudication for standard claim types with no manual intervention required.
↗Claim volume growth no longer requires headcount growth. The platform handles increased throughput at marginal additional cost — redefining the economics of motor insurance operations.
↗Reduced processing time translates directly to faster claim settlements — improving customer experience, reducing complaints, and strengthening policyholder trust at every touchpoint.
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