Contact Center AI Risk Profiling During Fraud Review Calls
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Solution Overview
Problem
Existing systems suspend accounts unnecessarily when potential fraud is detected, causing inconvenience to legitimate users and extra work for institutions to reinstate them, due to abnormal transaction behaviors that are falsely identified as fraudulent.
Innovation Solution
Implementing a neural network-based AI model that analyzes call logs, transaction data, and context to generate an activity risk profile during a communication session, allowing proactive fraud detection and dynamic response actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection systems suspend accounts upon detecting abnormal transaction behavior, then fraud prevention capability is improved, but false suspension of legitimate users increases and user convenience deteriorates
Solution Approach 1:
The system dynamically adjusts account restrictions based on real-time risk assessment rather than applying static suspension rules. The AI model continuously evaluates transaction patterns and updates risk scores, allowing the system to transition between different states (full access, restricted access, suspension) based on current behavior, thereby reducing false suspensions of legitimate users while maintaining fraud prevention
Solution Approach 2:
The system changes the parameter of risk threshold from a fixed value to a dynamic value that adapts based on multiple factors including transaction amount, frequency, location, and user behavior patterns. This allows the system to adjust its sensitivity to abnormal behavior, reducing false positives for legitimate users while maintaining detection capability for actual fraud
2Reliability
If traditional systems suspend accounts for potential fraud, then security is improved, but institutional workload increases due to reinstatement processes
Solution Approach 1:
The system implements automated risk assessment and account management where the AI model automatically evaluates transactions, determines appropriate restrictions, and can autonomously reinstate accounts when risk levels decrease. This self-service capability eliminates the need for manual review and reinstatement processes, significantly reducing institutional workload while maintaining security
Solution Approach 2:
The system establishes a feedback loop where transaction outcomes and risk assessments are continuously monitored and fed back into the AI model. This allows the system to learn from past decisions and automatically adjust account restrictions based on evolving risk patterns, reducing the need for manual intervention and improving operational efficiency
3Measurement precision
If AI models analyze multiple data dimensions for fraud detection, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The AI model serves multiple functions simultaneously: it analyzes transaction patterns, evaluates user behavior, assesses geographic risk, detects device anomalies, and generates risk scores all within a single unified system. This multi-functionality allows comprehensive fraud detection across multiple data dimensions without proportionally increasing system complexity, as the same AI infrastructure handles diverse analytical tasks
Data Source
AI summary
An example operation may include one or more of implementing a trained artificial intelligence (AI) model using a neural network training capability with at least one of call log data of calls determined to have an activity risk, activity risk data, activity context, and model feedback data, capturing audio from an ongoing telephone call, converting the audio from the ongoing telephone call into text, obtaining context of an activity from a computing device, executing the trained AI model to generate an activity risk profile based on the text and the context of the ongoing telephone call, and executing an action during the ongoing telephone call based on the activity risk profile.