Customer Case Study: Reduction in Auto Claim Costs
Leading Global Insurer sees a multi-million $ reduction in auto claim costs when using Causal AI
Top Global Insurer
Auto Insurance
Insurance Claim Costs
Multi-million $ reduction in auto claim costs
The Challenge
The leading global insurer processes $100s of millions in motor insurance claims per year. They invested in a series of initiatives to reduce these costs, including a network of partner garages. They are interested in understanding how this network drives (if any) a reduction in auto claim costs and how they can further reduce these costs through customer incentives.
However, traditional methods were unsuitable for solving this problem:
![](https://causalens.com/wp-content/uploads/2023/09/Screenshot-2023-10-26-at-15.24.35.png)
Many potential confounders
There were many potential confounders, e.g., are claims for network garages cheaper than equal claims, or do customers leverage the partner network only for minor repairs?
![](https://causalens.com/wp-content/uploads/2023/09/Screenshot-2023-10-26-at-15.24.35.png)
Can't run 'what-if analyses'
They require the ability to run counterfactuals and interventions to develop optimal policies to reduce costs.
![](https://causalens.com/wp-content/uploads/2023/09/Screenshot-2023-10-26-at-15.24.35.png)
Not utilizing their team's domain knowledge
The customer’s team has a great deal of domain knowledge and requires an efficient way to integrate this into the modeling process.
Solution
The problem required a causal understanding, and the customer team turned to decisionOS for a solution. Using decisionOS, our Causal AI platform, they were able to:
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- Understand and quantify the causal drivers of claim costs, accounting for potential confounders.
- Run “what-if” analyses to develop new optimal policies to reduce costs.
- Integrate their domain knowledge into the modeling process, combining the best of domain experts and data-driven approaches.
Results and Benefits
Leveraging decisionOS, this leading Global Insurer was:
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- Able to quantify the causal impact of repairs taking place in-network, uncovering a multi-million dollar cost reduction opportunity.
- Able to identify how costs were impacted across different incident types and which incidents they should create incentives for in-network.
- Able to determine new enticements to drive down costs, identify which garages to keep or remove from their network, and create optimal pricing for policies.
- Able to identify, in the long-term, how inflation impacts different types of claims and how to adjust their policy pricing moving forward.
As a global insurer that processes $100mns in motor insurance claims annually, they must invest in technology to reduce these costs. By identifying, understanding, and quantifying the causal drivers of claim costs, running a “what-if” analysis to decide on optimal policies for price reduction, and maximizing the use of their team’s domain knowledge, this customer was able to identify multi-million $ cost savings.
By merging industry-leading technology and the customer’s domain expertise, this example highlights a clear path for Causal AI to be the technology that Global Leaders in the insurance sector invest in across multiple use cases.
![](https://causalens.com/wp-content/uploads/2023/12/tekton-O_ufcLVTAYw-unsplash.jpg)
![](https://causalens.com/wp-content/uploads/2023/12/tekton-O_ufcLVTAYw-unsplash.jpg)