Adaptive Customer Care Workflow for Credit Abuse Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless communication service providers face a financial drain due to customers who 'game the system' by frequently requesting credits, which can be difficult to distinguish from sincere customers seeking help, leading to potential unfair categorization of legitimate users as manipulators or abusers of customer care.
Innovation Solution
A method and system that analyze call records to identify patterns of frequent calls and credit requests, tagging suspicious accounts and adapting automated workflows to offer expert callbacks, delayed credits, and altered IVR menu navigation to deter abusive behavior while maintaining service quality for genuine users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated workflows are used to handle customer care calls, then service efficiency is improved, but manipulative customers can exploit the system to obtain unmerited credits
Solution Approach 1:
The system performs preliminary analysis of call records and customer behavior patterns before processing credit requests. By pre-identifying manipulative customers through pattern recognition and tagging, the system prepares appropriate workflow adaptations in advance, preventing exploitation before it occurs while maintaining efficient automated handling for legitimate customers.
Solution Approach 2:
The system continuously monitors customer interaction patterns with customer care and provides feedback by updating tags and adjusting workflows. The adaptation application analyzes call records, identifies manipulative behavior patterns, and feeds this information back to modify the automated workflow in real-time, creating a dynamic system that learns and adapts to prevent credit fraud while maintaining service efficiency.
2Loss of energy
If strict monitoring is implemented to detect manipulative customers, then financial loss is reduced, but service quality for legitimate users may be degraded
Solution Approach 1:
The system applies different levels of monitoring and workflow adaptation to different customers based on their behavior patterns. Legitimate customers experience standard efficient service, while only those tagged as manipulative trigger adapted workflows with additional verification steps. This localized application of strict monitoring protects financial interests without degrading service quality for the majority of genuine users.
Solution Approach 2:
The system dynamically changes workflow parameters such as credit approval thresholds, callback requirements, and IVR navigation options based on customer tags. For manipulative customers, parameters are adjusted to require expert callbacks or delay credit processing, while legitimate customers experience unchanged streamlined processes. This parameter adaptation reduces financial loss without impacting service quality for honest users.
3Measurement precision
If manual review processes are used to verify customer legitimacy, then accuracy in identifying manipulative customers is improved, but processing time increases
Solution Approach 1:
The system applies full manual review processes only partially - specifically to customers tagged as manipulative or suspicious. The majority of customers receive automated processing without manual intervention. This selective application of thorough verification maintains high accuracy in identifying manipulative customers while minimizing time loss for the overall customer base.
Solution Approach 2:
The adaptation application serves as an intermediary that automatically analyzes call records and patterns to identify manipulative customers, replacing the need for manual review of all customers. This automated intermediary performs the verification function with high accuracy for suspicious cases while allowing rapid automated processing for legitimate customers, thus improving precision without significantly increasing processing time.
Data Source
AI summary
A method of adapting customer care handling automated workflows. The method comprises creating records of calls by a subscriber to a customer care handling system, analyzing the records of calls by a customer care handling adaptation application, comparing by the application a frequency of calls to customer care and a frequency of account credits granted to the subscriber to a correlation threshold, where the correlation threshold is randomly varied within predefined correlation values, tagging the wireless communication service account of the subscriber by the application as manipulative of customer care, receiving a call from the subscriber to the customer care handling system, determining by the customer care handling system that the subscriber is tagged as manipulative of customer care, and adapting the handling automated workflow for the subscriber by the customer care handling system based on the determination that the subscriber is tagged as manipulative of customer care.