Use Case

Automation underwriting in life insurance

A leading life insurer in the US wanted to reduce the need for invasive medical exams during the underwriting process to save costs and improve the customer experience. By harnessing AI and internal data, a better risk predictor score was developed to automate the underwriting process.

1

Challenges

  • Majority of the life insurer’s clients were required to undergo invasive medical tests.
  • 93% of the cases required a doctor’s statement.
  • ¬†Mandatory requests for lab tests were expensive and time consuming.

2

Opportunities

  • Harness insights from other indicators to more accurately predict risk.
  • Build models around company data to better predict risk e.g. behavioural and financial.
  • Integrate new and existing 3rd party data sources e.g. consumer footprint and credit health.

3

The Solution

  • An automated underwriting process built on AI to predict risk.
  • Majority of new applications now categorised as non-invasive and can be processed automatically.
  • The insurer has reduced operational costs and increased efficiency.
  • ¬†Increased revenue by reducing drop-out from new customers.

THE RESULTS

The insurer has been able to realise significant improvements to business efficiency while improving the customer experience.

X 0

Increase in non-invasive risk classification – from 7% to 70% of cases.

~ 0 %

of the non-invasive cases can now be automatically approved.

$ 0 M

Potential business impact in cost savings and reducing churn in the process.

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