AI Billing Workload Preemption in Call Centers
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Solution Overview
Problem
Large service providers face high call-center costs due to frequent customer inquiries about billing issues, which can be anticipated and potentially eliminated to increase customer satisfaction.
Innovation Solution
A system and method using AI models to analyze customer and billing data to identify potential billing issues before they result in calls to the call-center, allowing for proactive resolution and prevention of customer complaints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional call-center monitoring is used to handle billing complaints, then customer complaints are resolved, but call-center costs and workload increase
Solution Approach 1:
The system performs preliminary analysis of billing data using AI models to identify potential billing issues before customers call the call-center. By detecting problematic billing items in advance and notifying customers proactively, the system prevents complaints from reaching the call-center, thereby reducing workload while maintaining resolution reliability.
Solution Approach 2:
The system continuously monitors billing data and uses AI models to predict potential issues, creating a feedback loop that identifies problems before they become customer complaints. This feedback mechanism allows the system to adjust billing notifications proactively, reducing call-center workload while maintaining customer satisfaction.
2Measurement precision
If AI models are trained on historical data to predict billing issues, then proactive identification improves, but data collection and model training complexity increase
Solution Approach 1:
The system uses a single AI model that serves multiple functions: predicting billing issues, classifying customer types, and determining notification strategies. By making the AI model multi-functional, the system reduces overall system complexity while maintaining high prediction accuracy across different billing scenarios.
Solution Approach 2:
The system creates simplified representations of complex billing data through feature engineering, transforming raw billing records into standardized features that the AI model can process efficiently. This copying approach maintains prediction accuracy while reducing the complexity of data collection and processing.
Data Source
AI summary
A system, method, and computer program are provided for processing a billing item. In use, a first dataset is collected including a plurality of records. The first dataset includes customer records, billing records (with billing item(s)) for each of the customers, and call incident records (with calling customer identification, billing record identification, and billing item identification). Additionally, a first AI-model is trained using the first dataset to recognize at least one pair of a first customer type and a first billing item type, and an associated first probability that such pair results in a call to the call-center. Further, a second dataset is collected including new billing records. The first AI-model is used to detect at least one billing record in the second dataset having probability higher than a redefined threshold probability that the customer associated with the billing record will call a call-center.