SAS, a provider of business analytics software and services, has launched a new software, Social Network Analysis to help organisations fight fraud. The new software helps organisations to uncover hidden relationships between individuals and data; detect patterns and trends; and mine text and other unstructured data.
The company said, Social Network Analysis applies link analysis to fraud detection and prevention, marketing, and customer segmentation. It was released recently in the US and is presently available in Asia Pacific.
According to the company, using it within the company’s Fraud Framework, insurers and government agencies can pinpoint suspicious individuals and transactions and stop additional fraud. Telecommunications and bank marketers can measure and predict the influence of different customer segments on acquisition, retention and up-sells.
The company added that its Social Network Analysis helps investigators go beyond transaction and customer views to analyse all related activities and relationships within a network: shared addresses, phone numbers, employment information, account ownership and other transactional data.
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The software’s visualisation capabilities reportedly allow access to customer details and all related parties and networks, resulting in quicker case assessments and better dossiers. Social Network Analysis includes Net-CHAID technology, which allows fraud investigators and brand and marketing managers examine and manipulate large clusters for analysis.
The company’s social media analysis software, to be launched in the fourth quarter, is supported by Sentiment Analysis Manager from its Teragram division. Through natural language processing and text mining, the software captures consumer product reviews and brand comments from mainstream sites such as Amazon and Overstock, message boards, and social media outlets like blogs and Twitter, the company said.
Sentiment Analysis Manager locates and analyses digital content in real time to determine the writer’s emotion, spot changing trends, and uncover potential product defects at an early stage.
