Fraud Importance Engine for Prioritized Call-Center Detection
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Solution Overview
Problem
Conventional fraud detection systems in enterprise call centers fail to account for the dynamic nature of fraudulent activities and do not prioritize important fraud events based on their impact, treating all fraud events equally, which results in suboptimal detection performance.
Innovation Solution
A fraud detection engine with a machine learning architecture that calculates an importance score for fraud events based on user-defined attributes, prioritizes fraud alerts, and adjusts ML models using feedback to enhance detection performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fraud detection systems treat all fraud events equally, then the system operates with uniform detection thresholds, but the detection performance deteriorates for high-impact fraud events
Solution Approach 1:
The patent applies local quality by assigning different importance scores to different fraud events based on their specific characteristics and impact. The system evaluates each fraud event individually using multiple attributes (fraud type, activity, channel, account information) and assigns a customized importance score, allowing high-impact events to receive more intensive detection resources while maintaining uniform processing for lower-impact events.
Solution Approach 2:
The system implements dynamics by continuously updating fraud importance scores based on evolving fraud patterns and feedback from detection outcomes. The importance scores are not static but adapt over time as the system learns from new fraud events and adjusts its detection priorities, enabling the system to respond dynamically to changing fraud landscapes.
2Reliability
If the system prioritizes high-impact fraud events using importance scores, then detection effectiveness improves, but system complexity increases
Solution Approach 1:
The patent segments the fraud detection system into distinct functional modules: fraud event identification, attribute evaluation, importance score calculation, and prioritized detection. Each module handles a specific aspect of the detection process, making the overall complex system manageable through modular design. The segmentation allows independent optimization of each component while maintaining system-wide coordination through the importance score mechanism.
3Measurement precision
If the system uses multiple attributes to calculate fraud importance scores, then the ability to distinguish important fraud events improves, but the computational requirements increase
Solution Approach 1:
The system applies partial action by selectively evaluating attributes based on the specific fraud event context. Rather than uniformly processing all attributes for every fraud event, the system identifies and prioritizes the most relevant attributes for each event type, reducing unnecessary computational overhead while maintaining accurate importance scoring for high-impact events.
Data Source
AI summary
Embodiments described herein provide for a fraud detection engine for detecting various types of fraud at a call center and a fraud importance engine for tailoring the fraud detection operations to relative importance of fraud events. Fraud importance engine determines which fraud events are comparative more important than others. The fraud detection engine comprises machine-learning models that consume contact data and fraud importance information for various anti-fraud processes. The fraud importance engine calculates importance scores for fraud events based on user-customized attributes, such as fraud-type or fraud activity. The fraud importance scores are used in various processes, such as model training, model selection, and selecting weights or hyper-parameters for the ML models, among others. The fraud detection engine uses the importance scores to prioritize fraud alerts for review. The fraud importance engine receives detection feedback, which contacts involved false negatives, where fraud events were undetected but should have been detected.