The CEO Roadmap to Tackling Methane Emissions
Benchmark & Reverse Engineering
Sia Partners has developed skills in reverse engineering that facilitate the reconstruction of pricing models, using only a set of data from an initial “unknown model”. With internal or external databases, these methods 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 internally developed tools 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 the determination of variables that 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 are identified. The database is then cleaned to remove 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, as well as multiple variables such as the location variable.
With the tightening of regulatory constraints and commercial competition, price 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.