Inputs and Model Settings
Enter predictors and coefficients. This calculator applies a logistic function to convert a linear score into a probability.
Formula Used
This calculator uses a logistic model, common in claim propensity and risk scoring. First, it computes a linear score:
- p is the predicted probability (0 to 1).
- z is the log-odds score; larger values increase probability.
- Expected claim cost is computed as p × AverageClaimCost.
How to Use This Calculator
- Enter predictor values for the customer or scenario you want to evaluate.
- Paste coefficients from your trained model or scorecard into the coefficient fields.
- Set a decision threshold to align with underwriting or triage policies.
- Press Submit. The probability and decision label will appear above the form.
- Use CSV or PDF export buttons to save results for review.
Example Data Table
Use for quick validationThese sample rows illustrate typical values used in claim probability modeling. You can export them as CSV after you calculate once.
| # | Age | Income | Tenure | Prior Claims | Amount | Risk Score |
|---|---|---|---|---|---|---|
| 1 | 28 | 45000 | 1.2 | 0 | 1200 | 42 |
| 2 | 41 | 82000 | 5.8 | 1 | 3800 | 58 |
| 3 | 35 | 60000 | 3.0 | 2 | 2500 | 55 |
| 4 | 52 | 97000 | 9.5 | 0 | 7000 | 61 |
| 5 | 23 | 32000 | 0.7 | 1 | 900 | 49 |
Tip: Keep coefficient units consistent. If you switch income to thousands, adjust b_income accordingly.
Model purpose in claim operations
Claim probability scoring estimates the likelihood that a policyholder files at least one claim within a defined horizon. Teams use it to prioritize reviews, route high risk cases, and budget reserves. This calculator implements logistic scoring, a standard approach because it outputs probabilities between 0 and 1 and remains interpretable under governance reviews. When paired with outcome dates, the same score can support triage and reserving, as long as the horizon is clearly documented.
Predictor signals and data hygiene
Common inputs include age, tenure, prior claim counts, and a normalized risk score. Prior claims are typically sparse and zero heavy, so consistent counting windows matter. Tenure is often right skewed, making outlier checks useful. Keep currency fields in one unit and avoid mixing monthly and annual income values. Risk scores usually span 0–100; if your score is 0–1, multiply by 100 or adjust the coefficient scale.
Coefficient management and calibration
Coefficients represent learned effects from historical labeled data. If features are rescaled, coefficients must be rescaled. For example, entering income in thousands requires multiplying the income coefficient by 1,000. Store coefficient versions, training periods, and feature definitions so results can be reproduced during audits and model monitoring. After deployment, calibrate probabilities using reliability plots or isotonic methods, especially when claim rates shift.
Thresholds, triage, and expected cost
The decision threshold converts probability into an action label, such as “Likely Claim” or “Unlikely Claim.” A higher threshold reduces false positives but can miss emerging risk. Pair the threshold with expected claim cost, computed as probability times average claim cost, to rank cases when capacity is limited. Many teams choose thresholds by targeting a review rate, such as the top 5–15% of policies, then confirm lift versus a random sample.
Validation using exports and scenarios
Use scenario testing to confirm directionality: increasing prior claims, risk score, or scenario amount should generally raise probability, while longer tenure may lower it. Export result CSV files to compare multiple customers consistently. Save PDF reports as evidence for pricing changes, underwriting decisions, or investigation referrals. Recheck performance over time with AUC, precision at k, and drift checks on key predictors.
FAQs
What does the predicted probability mean?
It is the model’s estimated chance of a claim for the provided inputs. It summarizes historical patterns captured by your coefficients, not a certainty for an individual case.
Can I paste coefficients from my trained model?
Yes. Enter your intercept and feature coefficients exactly as trained. Keep input units consistent with training, then submit to calculate probability and export CSV or PDF outputs.
How do I select a good threshold?
Choose a cutoff that matches review capacity and loss tolerance. Start with a target review rate, evaluate precision at that rate, and adjust as claim frequency and operations change.
Why is the log-odds score shown?
Log-odds is the linear score before the logistic transform. It helps compare scenarios, understand extreme probabilities, and diagnose whether coefficients or units are causing instability.
What is expected claim cost used for?
Expected cost equals probability times the average claim cost you provide. It links likelihood to financial impact so you can prioritize cases when multiple predictions are similar.
Is this a replacement for full model validation?
No. It is a scenario and reporting tool. Validate models with holdout testing, calibration checks, and drift monitoring, then use this page to communicate and document decisions.