Real-Time Call Intent Differentiator for Fraud Detection
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Solution Overview
Problem
Enterprise call centers face challenges in detecting and addressing fraud and efficiently handling genuine customer inquiries due to high call volumes, leading to potential losses and customer dissatisfaction.
Innovation Solution
A system and method using a computer processor to determine the intent of callers by analyzing parameters such as the number of calls from a single ANI, authorization methods, and fraud alerts, initiating real-time fraud prevention or customer service responses based on these determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the call center handles large volumes of calls, then productivity is improved, but fraud detection capability deteriorates
Solution Approach 1:
The system performs preliminary actions by analyzing call parameters and customer history before the call reaches human agents. Fraud risk assessment is conducted in advance using automated systems that evaluate multiple parameters including call frequency, authorization methods, and customer account history, enabling fraud detection to scale with call volume without requiring manual intervention for each call.
Solution Approach 2:
An automated fraud detection system acts as an intermediary between incoming calls and human agents. This intermediary layer analyzes call parameters, checks customer history, and assesses fraud risk before routing calls to appropriate handlers, allowing the system to maintain high productivity while improving fraud detection capability through automated decision-making.
2Reliability
If the call center implements comprehensive fraud detection, then fraud prevention is improved, but device complexity increases
Solution Approach 1:
The fraud detection system is segmented into multiple independent modules that each handle specific aspects of fraud detection: call parameter analysis, customer history checking, authorization method validation, and real-time risk assessment. This segmentation allows the system to achieve comprehensive fraud prevention through coordinated simple modules rather than a single complex system.
Solution Approach 2:
The system employs a multi-functional call analysis platform that performs various functions including fraud detection, customer service quality monitoring, and real-time decision-making within a unified framework. This universal approach prevents fraud while managing complexity by consolidating multiple detection functions into a single integrated system rather than separate specialized systems.
3Loss of time
If the call center responds quickly to genuine customers, then customer satisfaction is improved, but fraud risk increases
Solution Approach 1:
The system performs preliminary fraud risk assessment before routing calls to human agents, analyzing multiple parameters including call frequency from the same number, authorization methods used, and customer account history. This preliminary action filters out high-risk calls before they reach agents, enabling quick response to genuine customers while preventing fraudsters from exploiting rapid response systems.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor call patterns, customer interactions, and fraud indicators in real-time. This feedback loop allows the system to adjust its responses dynamically, maintaining fast service for legitimate customers while adapting to detect and prevent fraud attempts that may emerge from the high-volume, fast-response environment.
Data Source
AI summary
Systems and methods for detecting fraud when receiving multiple calls from a caller to an enterprise's call center. Methods may include using a computer processor to determine if the caller's automatic number identification (ANI) is associated with more than five calls received by the call center within a current calendar day. If so, methods may include using a computer processor to determine the following parameters. The incoming calls from the ANI inquired about more than five customer accounts. The call refers to a customer account that has received multiple calls per month for less than four out of the last twelve months. The ANI of the incoming call is different from any phone numbers associated with the customer account. The response measure may be a fraud prevention response measure when one or more of the parameters are true, or a customer service response measure when none are true.