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Insurtech Risk Management ML Model for Natural Disaster risks mitigation
Uniсsoft has built a machine learning model that helped an insurtech company to calculate natural disaster risks at 93% accuracy.
About the client
Business Challenge
Many regions of developed countries, such as the United States or Japan, are subject to frequent natural disasters. The real estate insurance market has traditionally taken into account these risks, which were reflected in the price of insurance services for a client.
- Names of districts and locations are not unified in the main datasets — the client could not create a disaster forecasting model simply by comparing two datasets.
- Data in these datasets could not be compared with data on other regions and districts that are already in the insurance platform (different names and codes for the same areas).
- The client’s data sources were missing location information for multiple locations.
The solution
Unicsoft developed the project following the сross-industry standard process for machine learning and data science projects (a standard process for most ML projects). In total, the project went through six stages of development and implementation:
Understanding
Forestry previously completed the Business Understanding and Data Understanding steps. Before starting the work, Uniсsoft had a complete understanding of the business requirements and identified the databases and their potential problems.
Understanding
Forestry previously completed the Business Understanding and Data Understanding steps. Before starting the work, Uniсsoft had a complete understanding of the business requirements and identified the databases and their potential problems.
Preparation
We started with Data preparation which consisted of the following steps:
- ● Clean up junk data
- ● Compose a training set and match SoV to Results in a unified set
- ● Run a statistical analysis on the structured dataset
In-product integration. Although the integration of the model into the client’s product was not the original task, Uniсsoft carried out all the preparatory activities so that the model could be integrated and running in a few days.
Model assessment. Unicsoft implemented new techniques in order to model the data and verify the ability of the model to achieve the desired result. The final product demonstrated high accuracy of the ML model – it layed within 93%.
Data science and ML modeling phase. The customer database operated on two major external databases that categorized regions and predicted disasters. However, these external databases had unstructured info that didn’t match the data in the customer’s database. Unicsoft had to develop a model that could read information from these databases and simulate human behavior in order to identify a correspondence between the results of disaster modeling in external datasets and for the data on the regions in the customer’s platform.
Forestry previously completed the Business Understanding and Data Understanding steps. Before starting the work, Uniсsoft had a complete understanding of the business requirements and identified the databases and their potential problems.
Forestry previously completed the Business Understanding and Data Understanding steps. Before starting the work, Uniсsoft had a complete understanding of the business requirements and identified the databases and their potential problems.
We started with Data preparation which consisted of the following steps:
● Clean up junk data
● Compose a training set and match SoV to Results in a unified set
● Run a statistical analysis on the structured dataset
Data science and ML modeling phase. The customer database operated on two major external databases that categorized regions and predicted disasters. However, these external databases had unstructured info that didn’t match the data in the customer’s database. Unicsoft had to develop a model that could read information from these databases and simulate human behavior in order to identify a correspondence between the results of disaster modeling in external datasets and for the data on the regions in the customer’s platform.
Model assessment. Unicsoft implemented new techniques in order to model the data and verify the ability of the model to achieve the desired result. The final product demonstrated high accuracy of the ML model – it layed within 93%.
In-product integration. Although the integration of the model into the client’s product was not the original task, Uniсsoft carried out all the preparatory activities so that the model could be integrated and running in a few days.
The results
Forestry and their client were able to get a fully working ML model ready for integration with the client's product ahead of schedule and under budget.
The client noted how surprised they were that they hardly needed to get involved in the work as well as the proactivity and professionalism on the part of the Unicsoft team.
- Forestry avoided having to hire expensive in-house engineers. Unicsoft provided a dedicated team and specialists with experience based on dozens of ML projects. It is very hard to assemble such a team employing a traditional hiring process, given the competition on the market.
- Forestry's client was impressed with the speed and quality of the work, which had a positive effect on their business relationships.