Predict and prevent student insurance cancellations from fraud
Indie-scale student life insurance providers face 40% policy cancellations due to high fraud rates in quotes, with no affordable fraud detection tools available.
CancelGuard uses ML to predict cancellation risk from quote patterns and historical data, scoring leads pre-policy. Providers prioritize low-risk quotes, follow up on high-risk with extra verification. Cuts fraud losses affordably for small brokers.
Indie-scale providers and small independent brokers offering student life insurance quotes
Predictive cancellation modeling trained on 10k+ anonymized student policies
friendly
ML score for cancel risk on new quotes
Dashboard of fraud patterns in your quotes
Auto-tag quotes as low/medium/high risk
Auto-generate verification tasks for high-risk
Compare predicted vs actual cancellations
Send risk scores to your CRM
Compare your fraud rate to industry avg
Natural language fraud tips
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| model_version | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| quote_data | jsonb | No |
| risk_score | int | No |
| outcome | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| threshold | int | No |
| actions | jsonb | No |
Relationships:
/api/predict-cancelGet risk prediction for quote
/api/predictionsList predictions with outcomes
/api/workflowsConfigure risk thresholds
No workflows
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 60 | 6% | $175 | $2,100 |
| Month 6 | 350 | 12% | $1,200 | $14,400 |
AI spots fraud in student quotes – retain 40% more policies effortlessly.
Target indie brokers via Twitter search 'student insurance cancellations', DM with free prediction on their shared data. Run $100 LinkedIn ads to brokers, funnel to demo call.
Ecom approvals
Not predictive for insurance
Cancellation-specific ML
Device fingerprint
Complex for indies
Simple, predictive focus
Network effects from shared prediction data improving all models
AI prediction costs dropped 90%, brokers digitizing post-2023 regs
Poor prediction accuracy initially
Hybrid rules + ML
Brokers ignore predictions
Outcome tracking proof
ML hosting costs
Serverless scaling
Success: Model >70% accuracy
Success: 20% churn reduction
Success: 5% paid conv
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