Case Study

Underwriting decision engine that avoids evidence costs for a
life insurance company and enables 30% straight-through processing

Business Objective

Our client is a leading US-based provider of insurance and financial services products including life insurance, retirement plans, and mutual funds.

The underwriting team was overloaded with health screening of incoming applications through a traditional invasive process requiring extensive lab tests to identify high-risk applicants. The client wanted to create an automated process that enables straight-through processing of applications of high-risk individuals.


  • The process needed to be non-invasive (without blood testing) for cases where good risk prediction is feasible with non-invasive evidence data such as MVR, MIB, RX
  • Automated risk assessment required developing white box interpretable predictive models and triangulation from multiple data sources including external data
  • Reconciling and deriving KPIs definition from basic evidence data sources ( MVR, MIB, RX, etc. ) required working widely with the business teams

Solution Methodology

Developed an augmented underwriting solution framework comprising:

  • Robust KPIs from various sources of basic evidence data (MVR, MIB, RX) to augment traditional application data
  • Submodels to feed into overall risk triaging process:
    — Smokers model to identify smokers who do not declare their habit in the application.
    — Cholesterol/HDL model to predict cholesterol and HDL levels of applicants.
  • Acquired additional data from Commercial Auto Bureau to understand various characteristics such as speed violations, and other forms of traffic violations, indicating propensity of risky on-road behavior.
  • Predictive classification models to triage applications through the use of the above-mentioned submodels as well as derived KPIs from MVR, MIB, RX data
  • Leveraging the models and derived KPIs to predict high and low-risk applicantsto enable STP (straight-through processing) approval and rejection

Business Impact

  • ~30% of applications processed straight through post implementation of the automated framework
  • Lab test cost reduced by about 25%
  • Over $2M annual savings realized from avoided lab tests.
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