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Success Case by Tekne

Predicting insurance policy claims

Insurtech Industry

Problem Definition

An insurance company in the US is looking to:

  • Detect and predict patterns in policy claims.
  • Reduce the % of claims in its business portfolio.
  • Improve profits by avoiding insuring high-risk cars.

Problems faced: the currently percentage of claims is 5%.

  • Difficulty in understanding the distribution of claims and their factors.
  • Complexity in identifying probability patterns in claims.
  • Knowledge based mostly on unverified hypotheses.

Objective

Therefore, it was necessary to achieve greater use of the data from their systems, and to do so they aimed to:

  • Organize data to draw conclusions, detect patterns and predict behaviours.
  • Verify existing hypotheses about claims.
  • Generate and contrast new hypotheses and insights.
  • Develop a predictive model to estimate the probability of claims over the next 6 months.

Proposed Solution

We work together with the company proposing:

  • Implementation of predictive models to predict policy claims: random forest and logistic regression using 36 months of data.
  • Deployment of the models via API and integrated into its origination platform.

Work continuity:

  • Advice on good data management practices.
  • Implementation of tools for policy management.
  • Analysis to obtain valuable insights.

Architecture and Processes

Implemented architecture

Model diagram

Results

After working with us, the company now has:

  • A standard application model for claim detection.
  • Insights on which aspects influence the making of claims.
  • Predictive scoring for analyzing the behavior of customer policies and establishing different prices according to behavior.
  • Predictive scoring for carrying out campaigns in sector where the probability of claims is lower.

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