Matmut: a growing need for digitalization
Matmut is a mutual insurance company offering a wide range of insurance products for individuals and professionals (vehicles, home, personal injury, etc.).
To meet the growing need for digitalization in the insurance sector, Matmut wanted to have a solution to develop numerous data products. They were facing several challenges:
No willingness to adopt a build approach
Matmut did not want to invest in building a data solution of its own, which would be a heavy burden in terms of maintenance. They needed a turnkey solution to start immediately.
Many manual processes
The insurer was limited by its often manual data processing, which did not allow it to acquire a deep enough level of analysis to detect intent or recommend services, for example.
This became even more critical as the volume of data generated by the company grew.
Data profiles are too rare
Matmut had a very strong in-house business expertise, but lacked specialized profiles in disciplines such as data science or data engineering, which are crucial to progress on innovative use cases.
The DataOps Platform is the cornerstone of Matmut's data strategy, enabling it to develop and deploy all of its use cases in production.
A three-phase collaboration
A three-phase collaboration: the launch of initial experiments in 2016, the large-scale industrialization of use cases and then the integration of Saagie at the heart of Matmut’s production standards.
A multitude of projects in production
More than 15 use cases deployed in production (about 30 in the backlog) representing more than 1,000 processes and increasing operational efficiency, member satisfaction and sales, while reducing costs.
These use cases cover, for example, the recommendation of repair partners, the optimization of customer support via Machine Learning, risk measurement and LCBFT compliance.
A personalized accompaniment
Strong daily collaboration between Matmut’s data teams and Saagie’s Professional Services, with 25 Saagie users at Matmut (data, IT and business decision-makers) and constant support for business use cases.
You want to go into production and launch your data projects?