Key takeaways
- 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.