Skip to main content

Benchmark & Reverse Engineering

Sia Partners has developed skills in reverse engineering that allow reconstcuting pricing models with only a set of data coming from an initial “unknown model”. With internal or external databases, the methods used to allow to create an accurate benchmark.

Sia Partners has developed skills in Reverse Engineering to reconstruct pricing models from a dataset of a previously unknown model. These datasets can come from various sources (data capture, actual loss experience of an insurer, etc.).

Our skills in Reverse Engineering also make it possible to highlight the influence of the explanatory variables, for example we can extract pricing adjustments for the location variable.

Our approach is based on tools developed internally and is articulated according to the following four stages:

1 – Database

  • Profiles (predictive variables)
  • Rates (variables to predict)
  • Insurer / Open Data database
  • Data capture

2 – Variable analysis

  • Univariate analysis: allows to determine the variables which have an impact on pricing.
  • Bivariate analysis: used to build a location variable for example

3 – Base optimisation

Following the previous step, the variables impacting pricing were identified. A cleaning of the database is then carried out to remove the duplicates.

4 – Modelling

Simulation of several pricing models (cost-frequency, GLM, Machine Learning) to identify the different additive models and deduce the final pricing formula. This step makes it possible to determine the profiles attached to each model used, as well as the multiplicative variables (the location variable for example).

With the tightening of regulatory constraints and commercial competition, pricing positioning has become a major issue for social protection players. This is why Sia Partners has developed Reverse Engineering methods to support its customers in the creation of new products, ensuring coherence in their product offerings and in integrating market norms.