OLAP-Based Customer Behavior Profiling for Telecommunication Fraud Detection
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Solution Overview
Problem
Current methods for detecting telecommunication fraud are inefficient, failing to generate personalized caller profiles, taking too long to detect suspicious activity, struggling with high call volumes, and unable to provide up-to-date reports, leading to delayed detection and unrecoverable losses.
Innovation Solution
An OLAP-based system and method for profiling customer behavior that processes call records to generate personalized calling pattern cubes, compares them with known fraudulent patterns, and automatically updates profiles to detect suspicious activity in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consultants manually analyze calling records using prior art tools, then fraud detection can be performed, but the detection time is too long (six months to a year) allowing perpetrators to escape
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated electronic system that uses a profile engine and multidimensional cubes to automatically analyze calling records, compare them against fraudulent patterns, and generate real-time fraud detection reports, eliminating the time-consuming manual review process
Solution Approach 2:
The system pre-establishes multidimensional cubes containing fraudulent calling patterns and baseline caller profiles before analysis is needed. When new calling records arrive, the system immediately compares them against these pre-prepared reference structures, enabling instant fraud detection without requiring time-consuming analysis from scratch
2Measurement precision
If consultants analyze all calling records to detect fraud, then comprehensive fraud detection is achieved, but the sheer volume of calls (millions per day) makes it impossible to handle
Solution Approach 1:
The patent segments the massive calling record data into manageable multidimensional cubes organized by different dimensions (time, caller ID, callee ID, call duration, etc.). This segmentation allows the system to process and analyze specific segments independently and in parallel, making the overwhelming volume of millions of daily calls tractable
Solution Approach 2:
The system replaces manual consultant analysis with automated electronic processing that can handle millions of calls per day through the profile engine's ability to rapidly compare calling records against pre-established fraudulent patterns stored in multidimensional cubes
3Ease of operation
If coarse threshold detection methods are used, then the system is simple to operate, but it cannot generate personalized caller profiles and produces many false positives
Solution Approach 1:
The patent applies local quality by creating personalized baseline profiles for each caller that capture their unique calling behavior patterns. Instead of applying a single coarse threshold to all callers, the system tailors the analysis to each individual's normal behavior, allowing accurate detection of anomalies specific to each caller while maintaining automated operation
Solution Approach 2:
The system dynamically adjusts detection parameters by establishing personalized baseline profiles for each caller based on their historical calling patterns. These adaptive parameters (normal calling hours, typical duration, frequent contacts) replace fixed coarse thresholds, enabling accurate fraud detection that adapts to each caller's unique behavior while remaining automated
4Measurement precision
If past calling records are analyzed manually, then fraud patterns can be identified, but the reports are not up-to-date and perpetrators have already moved to different providers or numbers
Solution Approach 1:
The patent implements continuous automated analysis where the profile engine constantly receives and processes new calling records in real-time, continuously comparing them against fraudulent patterns. This continuous operation ensures that fraud is detected as soon as suspicious patterns emerge, keeping the detection process current and preventing perpetrators from escaping before detection
Data Source
AI summary
An OLAP-based method and system for profiling customer behavior that can be utilized to detect telecommunication fraud. First, call records are received. Next, a calling profile cube (e.g., a multi-customer profile cube) is generated based on the call records. A volume-based calling pattern cube (e.g., a calling pattern cube for each individual customer) is then generated based on the multi-customer profile cube. The volume-based calling pattern cube is then compared with known fraudulent volume-based calling patterns. If the similarities generated by the comparison reaches or exceeds a predetermined threshold, then the particular caller with the calling pattern being analyzed is considered suspicious. In this manner, suspicious calling activity can be detected, and appropriate remedial actions, such as further investigation or the cancellation of telephone services, can be taken.