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In auto insurance, one of the biggest challenges in claims management is deciding how to handle repairs. When 75% of claims result in a vehicle being sent to a repair shop, the insurer faces a dilemma: authorize the repair or pay the full value of the damage? A wrong decision can increase costs, extend resolution times, and affect the customer experience.
The Challenge: Standardizing Decisions in Complex Claims
Claims adjusters need accurate information to determine when it is more profitable and efficient to pay the damage or authorize a repair. This process, often based on experience, lacked standardized and robust criteria, affecting operational profitability.
The Infomedia Solution: A Recommendation Model for Claims Management
Infomedia implemented an intelligent recommendation model, a key advanced analytics solution for insurance that supports adjusters in real time. The solution analyzes multiple variables that directly affect the cost and efficiency of the claims process.
The model uses advanced techniques such as:
- Fitted Distributions: To evaluate and model risk.
- Statistical Inference and GLM Models: To generate robust predictions.
The system analyzes critical factors such as:
- Historical Repair-Shop Performance: Evaluates provider quality and efficiency.
- Expected Repair Duration: Predicts how long the process will take.
- Value at Risk: Estimates the potential increase in costs during the repair.
This allows the insurer to move from the "best guess" to strategic clarity. Predictive analytics is used for claims processing and management, helping prioritize resources and reduce overhead.
Results: 8% Reduction in Costs
The accuracy of the recommendations generated direct operational benefits. The insurer achieved an 8% reduction in repair costs. This was made possible by standardizing decision criteria so that all adjusters select the most profitable and efficient option, optimizing risk management and overall profitability.
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