How would you measure the success of Digit Insurance's smartphone self-inspection for motor claims?

Introduction

One of Digit Insurance's best-known innovations is smartphone-enabled self-inspection: instead of waiting for a surveyor, a policyholder records photos or video of the damaged vehicle with a phone, and Digit's team, increasingly helped by AI damage assessment, reviews it remotely. Digit uses similar self-inspection when a lapsed motor policy needs to be renewed. Measuring the success of self-inspection for motor claims requires balancing speed and convenience with accuracy and fraud control. I'll use a structured success metrics framework.

Step 1

Product Context

Self-inspection lets a customer who reports a motor claim complete the inspection on their phone. The app or a web link guides them to capture the damaged parts, vehicle number, chassis number and odometer. Images go to Digit's claims team, where AI models can flag damage type and severity and a claims assessor approves the estimate. Smaller claims can be decided entirely on this basis; larger or suspicious ones still go to a physical surveyor, as IRDAI rules require surveys above certain claim sizes.

Key stakeholders:

• Policyholders: Want quick, fair claims without waiting for a surveyor.
• Digit: Wants lower claim-handling costs, faster settlements, and accurate payouts.
• Garages: Want quick approvals to start repairs.
• Surveyors: Focus on complex or high-value claims.
• Regulator: Expects fair claim handling and timelines.

User flow:

1. Customer reports a claim in the app, website, call centre or partner channel.
2. Customer receives a self-inspection link.
3. Guided capture of photos and video.
4. AI and assessor review; estimate produced.
5. Approval and repair at a network garage (cashless) or reimbursement.

Product Lifecycle Stage: Maturity for the basic feature, Growth for AI-assisted assessment.

Software-specific context:

• Platform: App and browser-based capture (no app needed).
• Integration points: Claims management system, AI damage models, garage network, fraud analytics.
• Deployment: Frequent model updates; process changes need claims and compliance sign-off.