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High Operational Costs:
The manual processing of claims required multiple stakeholders at once, each handling different aspects of the claim, such as data entry, validation, assessment, and approval. This not only led to high labor costs but also increased the risk of errors in the workflow.
Slow Turnaround Time:
On average, processing and approving a claim took between 10 to 15 days. This lengthy process involved multiple steps, including initial claim intake, damage assessment, verification of policy details, and final approval.
Inconsistency in Damage Assessment:
The process relied heavily on individual surveyors to assess the damage being done to a vehicle. Different surveyors often provided varying assessments based on their judgment and experience(without a strong prerequisite in place).
Data Overload:
Each claim generated a heap of information, including detailed claim reports, numerous photos of the damage, repair bills, and communication logs. Organizing this much data manually was overwhelming and prone to errors.
Scalability Issues:
The sudden hike in no. of claims would overwhelm the staff, leading to delays in the entire processing flow.
In response to our client’s challenges, our AI team developed a custom Insurance automation workflow to automate the automobile claim surveying process, with several key components and phases:
Image Analysis and Damage Assessment with CV Technology: Advanced computer vision and machine learning algorithms were setup in place to analyze the uploaded images to assess the damage, while predictive models estimate repair costs based on historical data and industry standards.
Workflow Automation: The entire claim process, from data intake to final approval, is managed through AI/ML-backed automated workflows. Plus, the solution integrates seamlessly with the client’s existing systems for efficient data transfer and process integration.
Designed Custom Dashboards and Reporting: Deployed Power BI-powered real-time dashboards to monitor claim status, turnaround times, and overall system performance. Plus, notifications are triggered for any issues, allowing for prompt resolution.
Regular Audits: AI models are continuously trained and improved based on new data and outcomes alongside ongoing audits and performance reviews to ensure the system’s accuracy and efficiency from time to time.
Decreased the average claim processing time from 10-15 days to just 2-3 days, significantly improving efficiency.
Increased the accuracy of damage assessments by 25% using AI-driven image analysis, enhancing the overall quality of assessments.
Received positive feedback from 40% of customers regarding the improved speed and ease of the new claim process, highlighting the success of the implementation.
When scalability becomes an issue for any enterprise, it’s a signal to transform digitally and undergo an automation shift. The same goes for this insurance company from California. With automated workflows in place, our client successfully addressed the incoming challenges, providing better service to their policyholders and achieving significant cost savings for themselves.
