Anomaly Detection System for Billing Accuracy
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Solution Overview
Problem
Human errors in entering pricing data into billing systems can lead to billing anomalies, resulting in underbilling or overbilling customers, which causes revenue loss and customer dissatisfaction, especially when dealing with numerous customers and complex pricing structures.
Innovation Solution
A layered anomaly detection system that preprocesses pricing data from an unstructured to a structured format, uses outlier detection and machine learning models to identify anomalies, and includes a remediation system for correcting errors, flagging outliers and recommending corrections based on similar customer segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual entry of pricing data is used, then ease of operation is improved, but billing accuracy deteriorates due to human errors
Solution Approach 1:
The patent replaces manual mechanical data entry with automated machine learning-based anomaly detection systems. The system automatically detects and corrects pricing data errors using AI models, eliminating human errors in billing accuracy while maintaining ease of operation through automated processes.
Solution Approach 2:
The system performs self-service by automatically detecting anomalies in pricing data and generating corrections without human intervention. The machine learning models autonomously identify billing errors and suggest fixes, allowing the system to self-correct without manual checking.
2Manufacturing precision
If automated billing systems are used, then billing accuracy is improved, but device complexity increases due to multiple pricing structures
Solution Approach 1:
The patent segments the complex pricing data into manageable categories and uses separate machine learning models for different types of anomalies. The system divides the detection process into multiple layers, making the complex system more manageable and maintainable while preserving billing accuracy across diverse pricing structures.
Solution Approach 2:
The system introduces machine learning models as intermediary components between the complex pricing data and the billing output. These models act as mediators that simplify the processing of complex pricing structures by automatically interpreting and validating them, reducing the operational complexity burden.
3Manufacturing precision
If comprehensive anomaly detection is implemented, then billing accuracy is improved, but loss of time increases due to data processing requirements
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on historical billing data before actual anomaly detection is needed. The system pre-processes and structures pricing data in advance, so that when anomalies need to be detected, the models can quickly identify issues without time-consuming real-time analysis, thus reducing detection time while maintaining high accuracy.
Data Source
AI summary
Systems and methods for anomaly prediction are disclosed. An anomaly detection system identifies data generated for a customer. A first set of features for the customer are identified based on the data. The system performs an anomaly evaluation based on detecting a criterion. The anomaly evaluation may include identifying a customer segment based on the first set of features; identifying a distribution of values for the customer segment; determining, based on the distribution of values, whether a value associated with the first set of features satisfies a threshold; and in response to the determining that the value satisfies the threshold, invoking a machine learning model for predicting an anomaly for the customer based on at least a portion of the data. A notification may be transmitted about the anomaly to trigger an action for addressing the anomaly.