Pricing Optimization
“What premium maximizes retention and profitability?”
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A real-world example
What premium maximizes retention and profitability?
Insurers face a constant tension: price too high and you lose customers to competitors; price too low and you write unprofitable business. Traditional actuarial models set prices using loss-cost models with broad territory and classification factors, but miss individual-level price sensitivity. A policyholder with $5,400 in premium and a 0.15 loss ratio will tolerate a 5% increase but leave at 10%. Meanwhile, a policyholder with $1,800 in premium and a 0.65 loss ratio is price-insensitive because competitors will not offer a better rate. Getting this wrong costs top-20 insurers $200-500M annually in either lost profitable customers or underpriced risky ones.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects policyholders to their risk profiles, claims history, competitive market data, billing behavior, service interactions, and renewal outcomes. The model predicts the price elasticity for each policyholder: PH-6601 has high elasticity (competitive market, low loss ratio, recent rate increase) and should receive only a 3% increase, while PH-6602 has low elasticity (limited alternatives, higher risk profile) and can absorb 8%. The model optimizes across the portfolio to hit combined-ratio targets while maximizing retention of profitable customers.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
POLICYHOLDERS
| policyholder_id | premium | loss_ratio | tenure_years | lines |
|---|---|---|---|---|
| PH-6601 | $3,200 | 0.28 | 6.4 | Home + Auto |
| PH-6602 | $1,800 | 0.65 | 2.1 | Auto Only |
| PH-6603 | $5,400 | 0.15 | 11.2 | Home + Auto + Umbrella |
COMPETITIVE_RATES
| policyholder_id | best_competitor_rate | rate_gap_pct | num_quotes_found |
|---|---|---|---|
| PH-6601 | $2,900 | -9.4% | 3 |
| PH-6602 | $1,950 | +8.3% | 1 |
| PH-6603 | $5,800 | +7.4% | 2 |
RENEWAL_HISTORY
| policyholder_id | prior_increase_pct | renewed | year |
|---|---|---|---|
| PH-6601 | +6.0% | Yes | 2024 |
| PH-6601 | +11.9% | Pending | 2025 |
| PH-6602 | +4.7% | Yes | 2025 |
PORTFOLIO_TARGETS
| line_of_business | target_combined_ratio | current_combined_ratio | retention_target |
|---|---|---|---|
| Home | 92% | 95% | 88% |
| Auto | 97% | 101% | 85% |
| Umbrella | 85% | 82% | 90% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(POLICYHOLDERS.RENEWED = 'True', 0, 0, days) FOR EACH POLICYHOLDERS.POLICYHOLDER_ID ASSUMING RATE_CHANGES.PCT_CHANGE = 0.05
Prediction output
Every entity gets a score, updated continuously
| POLICYHOLDER_ID | CURRENT_PREMIUM | OPTIMAL_INCREASE | RETENTION_PROB | PROFIT_IMPACT |
|---|---|---|---|---|
| PH-6601 | $3,200 | +3.0% ($3,296) | 0.91 | +$96/yr |
| PH-6602 | $1,800 | +8.0% ($1,944) | 0.88 | +$144/yr |
| PH-6603 | $5,400 | +4.0% ($5,616) | 0.95 | +$216/yr |
Understand why
Every prediction includes feature attributions — no black boxes
Policyholder PH-6601 (Home + Auto, $3,200)
Predicted: Optimal increase: +3.0%, 91% retention probability
Top contributing features
Competitive rate gap (below market)
-9.4%
28% attribution
Prior increase magnitude (already +11.9%)
Compound fatigue
24% attribution
Low loss ratio (attractive to competitors)
0.28
20% attribution
Active competitor quoting activity
3 quotes found
17% attribution
Tenure and bundling stickiness
6.4yr, 2 lines
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Optimize individual-level pricing to improve combined ratios by 2-4 points while retaining 92%+ of profitable policyholders, generating $200-500M in annual value for a top-20 insurer.
Related use cases
Explore more insurance use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
