Vocal Sample Fraud Detection Using Benford's Law
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Solution Overview
Problem
Current automated IVR systems lack the capability to detect manipulated vocal samples, allowing fraudulent access to user accounts, as they do not utilize Benford's law to analyze the frequency distribution of leading digits in vocal data.
Innovation Solution
A system and method that transform vocal samples from the wavelength domain to the frequency domain, determine the digit frequency distribution, and calculate divergence from a predetermined frequency distribution, such as Benford's law, to detect manipulated vocal samples and execute security measures when divergence exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated IVR systems are implemented to reduce costs and wait times, then productivity and efficiency improve, but security vulnerability increases due to inability to detect manipulated vocal samples
Solution Approach 1:
The patent introduces an intermediary fraud detection system between the automated IVR system and the user account access. This intermediary layer analyzes vocal samples using Benford's law to detect manipulation without disrupting the automated IVR process, thus maintaining productivity while enhancing security reliability
Solution Approach 2:
The patent replaces traditional mechanical voice verification methods with a mathematical analysis approach based on Benford's law. Instead of relying on simple password verification or basic voice recognition, the system uses statistical analysis of frequency distributions to detect manipulated vocal samples, thereby improving fraud detection capability
2Ease of operation
If traditional voice verification is used in automated systems, then ease of operation is maintained, but security against fraudulent access deteriorates
Solution Approach 1:
The patent creates a statistical model (Benford's law distribution) that represents the expected pattern of genuine vocal samples. By comparing actual vocal samples against this statistical copy or model, the system can detect deviations indicating manipulation while maintaining the same simple voice-based interaction interface for users
Solution Approach 2:
The patent changes the verification parameter from simple voice pattern matching to statistical analysis of frequency distribution patterns. This parameter change enables the system to detect sophisticated manipulations that preserve basic voice characteristics, thereby enhancing security while maintaining ease of operation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively prevents fraudulent access by accurately identifying manipulated vocal samples and executing appropriate security measures, enhancing the security of IVR systems.
Implementation Method 1
The system may include performing a Fourier transformation of the first vocal sample from a wavelength domain into a frequency domain
Data Source
AI summary
A system may receive a communication from a user, which may include a vocal sample. The system may transform the vocal sample from a wavelength domain into a frequency domain. The system may determine a divergence of one or more amplitude values of the transformed frequency domain from a predetermined frequency distribution. According to some embodiments, the predetermined frequency distribution may be a Benford's distribution. When the divergence exceeds a predetermined threshold, the system may execute one or more security measures. The one or more security measures may include (i) transferring the user from an automated operator to a human operator, (ii) requiring second factor authentication from the user, and/or (iii) denying a user-initiated request.