Can you recommend a risk scoring engine for scoring mortality risk in life insurance underwriting?

Yes—if you want a mortality risk scoring engine for life insurance underwriting, the “best” choice depends a lot on whether you want:

1. A ready-to-deploy commercial model/API
2. A configurable underwriting rules + ML platform
3. A vendor that can build a custom mortality model with your data

Here are the main options I’d consider:

Strong commercial options

1. SCOR / Reinsurance-style underwriting analytics

• Good if you want actuarially grounded mortality risk segmentation.
• Often used by insurers/reinsurers for underwriting support and portfolio risk.
• Best for: carriers that want stronger actuarial alignment and custom model development.

2. LexisNexis Risk Solutions

• Common in insurance underwriting for identity, data enrichment, and risk signals.
• Not a mortality scoring engine by itself in the pure actuarial sense, but useful as input to underwriting models.
• Best for: augmenting underwriting decisions with external data.

3. EXL / Milliman / WTW / Trovata-style consulting + model building

• These firms can build or validate mortality risk models using your historical underwriting and claims data.
• Best for: organizations that need a custom mortality score rather than an off-the-shelf one.

4. Capgemini/SAS/Palantir-based underwriting decision platforms

• These are broader decisioning platforms that can host mortality risk models, rules, and workflow.
• Best for: carriers wanting an enterprise underwriting engine with explainability and governance.

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If you want a true “risk scoring engine”

A practical architecture usually includes:

• Data ingestion: application data, labs, Rx, MIB, EHR, motor vehicle report, APS, etc.
• Feature engineering
• Mortality model: GLM, survival model, gradient boosting, or calibrated scorecard
• Decision layer: rules, underwriting classes, referral thresholds
• Explainability: reason codes / adverse action support
• Model governance: monitoring, drift, validation, fairness review

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My shortlist by use case

Best for an insurer wanting a vendor solution:

• SAS
• EXL
• Milliman
• LexisNexis Risk Solutions as a data layer

Best for a reinsurer or carrier wanting actuarial depth:

• SCOR
• Milliman
• WTW

Best for building your own scoring engine:

• SAS Viya
• H2O.ai
• DataRobot
• Azure ML / AWS SageMaker / Databricks

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Important note

In life underwriting, mortality scoring is typically not just a generic “risk score.” You’ll usually want:

• mortality probability over a horizon
• relative risk vs. standard lives
• underwriting class recommendation
• reason codes and auditability

So if you’re buying software, I’d look for:

• actuarial model support
• explainable outputs
• integration with underwriting workflow
• validation/governance support
• ability to ingest third-party data sources

If you want, I can give you:

1. a vendor comparison table,
2. a recommended stack for a startup vs. carrier, or
3. a design for a mortality risk scoring engine you could build yourself.