/ Data Engineering

Case Study: Profitability Study of a Book of Commercial Properties

EXECUTIVE SUMMARY

Using open-source and freely available data science tools, we took a historical book of catastrophe-exposed commercial property insurance and assessed the segments of the market in terms of risk and profitability. In close collaboration with both the underwriting and actuarial teams, we help them set future strategic goals for that line of business.

OUTLINE

SITUATION

Rate pressure in the global commercial property market was a concern for a Lloyd’s Managing General Agent (MGA) with a large exposure to these risks. With an aim to improve their risk selection and mitigation procedures, they wanted to assess their historical book with a view to altering the mix of the business over the next few renewal cycles.

SOLUTION

An extensive amount of data engineering, cleansing and exploration work was required in the initial stages of the project, combining additional data sources to enrich the data. Locations covered by each policy were available, but claims were linked to policies. Policy terms such as attachment points and limits were accounted for within the model.

BUSINESS OUTCOMES

  • Visual exploration of the policy data and concentrations of risk
  • A number of simple dashboard-style slice-and-dice tools were built to assist future underwriting
  • Identification of loss making segments
  • Strategic underwriting objectives for renewal identified

PROJECT OUTPUTS

KEY FEATURES

  • Reproducible research methods allows for full auditability of the data engineering and exploration work critical to the project
  • Efficient data processing software avoided need for ‘big data’ infrastructure to perform analysis
  • Location less important than expected for attritional losses

IMPLICATIONS

  • Rate pressure had significantly eaten into expected profit of the book
  • Number of market segments below attritional loss-cost
  • Total premium written could be reduced without affecting profitability

KEY CLIENT BENEFITS

  • Reproducible research methods understood within the business
  • Improved audibility of work eased its take-up when revisited a year later
  • Enabled strategic decision-making based on quantitative reasoning

CONTACT DETAILS

Mick Cooney
M: +353 (0)87 819 5992
E: mcooney@agrippadataconsulting.com

Michael Crawford
M: +353 (0)87 996 7437
E: mcrawford@agrippadataconsulting.com

This post was a collaboration between

Mick Cooney, Michael Crawford

  • Mick Cooney

    Mick Cooney

    Mick is a theoretical physicist with a MSc in high performance computing and a PhD in quantitative finance. He runs a couple of data science meetups, is an expert in the use of R and is a Bayesian.

    More posts by Mick Cooney.

    Mick Cooney
  • Michael Crawford

    Michael Crawford

    Michael trained as an actuary but has always worked in IT. He is responsible for providing, project management, data-driven insights, machine learning and AI advice to our financial services clients.

    More posts by Michael Crawford.

    Michael Crawford
Case Study: Profitability Study of a Book of Commercial Properties

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