The Problem
When the economy takes a "downturn", one thing can be guaranteed to take an "upturn" - and that is fraud. As Internet security has become stronger, criminals and fraudsters are increasingly targeting soft options such as contact centers to gain access to accounts and personal information.
Too often, fraudsters can easily gain access to millions of accounts simply by quoting personal information either bought from criminal organizations or taken from social networking sites to a contact center agent. The problem is that the agent cannot differentiate between a legitimate caller and the fraudster, especially when the correct personal information is being quoted. As the uptake of mobility increases, and new channels such as the Internet and contact mediums like Web chat gain popularity, the problem is only going to worsen.
In this whitepaper, Auraya describes the application of its speaker-adaptive voice authentication technology to provide a solution to this problem. The solution allows the contact center agent to take a call and using the speech provided by the caller during the conversation, and compare his or her voice in the background against a "black-list" of known fraudsters and suspicious callers. Where there is a close match the agent or call center manager can be notified and appropriate action taken.
Simulating Fraudulent Calls
Set up: To assess the effectiveness of black-list detection, Auraya created a simulation. The first step was to configure the Auraya system to perform the "black-list" detection process. In this arrangement, speech spoken by a caller is compared against each of the acoustic models of the fraudulent "black-list" speakers. The output from this process is a list of scores representing how well the speech matches each model. The list is ranked in descending order, and a threshold is set to detect if the match is close enough to raise an alarm.
For this exercise a speech database of some 200 speakers collected in a telephone call center environment was used. The simulation had in three stages:
- Stage 1: Black-List Enrollment: This stage involved selecting ten speakers from the database to act as the "black-list" fraudsters. The selection was purely arbitrary and included both male and female speakers.
- Stage 2 - "Black-List" Detection: Once this was complete, the second stage involved processing the database of 200 speakers (which included the "black-list" speakers) and systematically comparing each speaker against each of the "black-list" voiceprints with the results of this process loaded into a database for further analysis.
The process generates two thousand authentication results, that is, 200 speakers each compared against the ten "Black-list" enrollments. The results generated by this process were then sorted into descending order, with the highest scores (closed matches) ranked at the top.
For more info please read: http://www.armorvox.com/blacklist-detection/