- H2O Driverless AI can build application risk models from your own applications, policies and loss history, with reason codes on every score.
- The score sorts the queue, not the outcome: your underwriter accepts, refers, rates or declines every application.
- H2O Document AI reads application forms and life underwriting evidence, such as attending physician statements, into the fields your model and your underwriter use.
- An agent assembles the referral file: score, reason codes, missing evidence and the guideline each flag comes from.
Every application waits for the same review
Your underwriters read a simple application with the same care as a complex one, because nothing tells them in advance which is which. The clean ones wait in the same queue as the ones that need real judgment.
The evidence arrives in pieces: an application form, prior policy and claims history, third-party reports and, in life lines, medical records and attending physician statements that someone has to read before the case can move. Rules engines help with the obvious cases, but they cannot weigh many factors at once, and a knockout rule says nothing about why an application is riskier than the next.
How automated underwriting risk scoring works
Your policy administration system and rules engine stay in place. The platform sits on top of them and reads the data they hold.
Step 1: bring the application data together
Join application answers with policy, claims and exposure history, plus the third-party data you are permitted to use for underwriting. Your compliance team decides which factors may be used in a score, and the model only sees those.
Step 2: read the evidence with document AI
H2O Document AI turns application forms, prior policies and life underwriting evidence into structured output that matches a schema your team defines. Intelligent character recognition reads scans, tables and handwriting for meaning, so a field lands in the same place whatever the layout.
Step 3: score the application
H2O Driverless AI trains classification models on your past applications and their outcomes. Automatic feature engineering finds the combinations of factors that separate low-risk applications from the ones that need a closer look.
Step 4: explain every score
Reason codes and Shapley values show which factors moved each score and by how much. Your actuaries can compare the model with their generalized linear models, and your underwriter sees the reasons, not just a number.
Step 5: assemble the file for the underwriter
An agent puts the score, reason codes, extracted evidence and a list of missing items into one referral file. Each flag cites the underwriting guideline it comes from, and the underwriter decides.
Step 6: document, deploy and monitor
Each model is documented as it trains and deployed as a scoring pipeline. H2O MLOps tracks model drift, so you retrain when the mix of applicants changes.
Life underwriting evidence
Life underwriting depends on documents that take time to read: attending physician statements, lab results and medical questionnaires. Document AI reads them into the fields your underwriting manual asks about, so your underwriter can ask where a value came from and get a cited answer. Your underwriter and medical director interpret the evidence; the extraction saves the reading and rekeying, not the judgment.
Where this fits your actuarial review
The model documentation supports your own actuarial, model risk and compliance review. It is not a rate filing and does not certify a model as fair; those reviews stay with your teams and your regulators. H2O Driverless AI can run disparate impact analysis across the groups you choose to test, for your own fairness review.