PET INSURANCE CASE STUDY
Cut fraudulent pet insurance claims by 35% for a leading UK insurer
We built an AI/ML fraud detection engine plus mobile & web claim submission, so claims are verified faster, flagged earlier and processed with less manual effort.
35% fewer Fraudulent Claims
50% Faster Processing
1.3 M+ Pets Covered
Pet Insurance Case Study
THE CHALLENGE
What was holding them back
Rising Fraud Pressure
A high volume of fraudulent claims was increasing financial losses and putting constant pressure on claims operations.
Slow, Manual Claims Processing
Manual claim submission and review workflows were time consuming, which delayed approvals and increased processing costs.
Poor Customer Experience
Longer turnaround times and inefficient claims handling led to customer dissatisfaction.
CLIENT SNAPSHOT
About the client
Industry
Pet Insurance
Geography
UK & Ireland
Service
AI & Automation
Existing Tools
Manual claim intake with human led reviews and limited automated validation
THE SOLUTION
Our Pet Insurance Solution
AI/ML fraud detection
- Trained a fraud classification model to score incoming claims and flag suspicious patterns early.
- Added explainable signals (why the claim was flagged) to help reviewers act quickly, not guess.
Streamlined online claim submission
- Built a mobile app and web portal for guided claim filing (fewer missing fields, cleaner inputs).
- Embedded fraud checks directly into the submission flow for instant risk scoring.
Workflow automation & reviewer productivity
- Automated routing, low risk claims move faster and high risk claims go to the right queue.
- Reduced manual touchpoints by standardizing documentation and validating data upfront.
Security & Governance
- Role based access for claims teams and reviewers.
- Traceability on decisions and risk flags to support internal audits and compliance.
THE IMPACT
Measurable Results
35%
Fraud reduction
drop in fraudulent claims
50%
Speed
reduction in claim processing time
✓
Efficiency: Faster resolution and improved customer experience
TECH STACK
Technologies Used
Python
Machine Learning
React Native
Spring
