NEW YORK, March 20, 2014 /PRNewswire/ -- IBM (NYSE: IBM) today introduced new software and services to help organizations use Big Data and Analytics to address the $3.5 trillion lost each year to fraud and financial crimes. Through sophisticated business expertise and analytics, organizations can take a holistic approach to address the financial losses caused by fraud while protecting the value of their brands.
As part of today's news, IBM launched its "Smarter counter fraud" initiative, drawing on the expertise and innovation from more than 500 fraud consulting experts, 290 fraud-related research patents and $24 billion invested in IBM's Big Data and Analytics software and services capabilities since 2005. The initiative extends IBM's leadership in Big Data and Analytics and Cloud to help public and private organizations prevent, identify and investigate fraudulent activities. Today's announcement comes at a time when a new generation of criminals are using digital channels – such as mobile devices, social networks and cloud platforms – to probe for weaknesses and vulnerabilities. The pace of this threat continues to accelerate – identity fraud impacted more than 12 million individuals in 2012, resulting in theft of nearly $21 billion, and each day the U.S. healthcare industry loses $650 million due to fraudulent claims and payments. To address these complexities, IBM is delivering new software that allows organizations to gain better visibility and take a more proactive, holistic approach to countering fraud. This includes the ability to aggregate Big Data across a variety of internal and external sources – including mobile, social and online – and apply sophisticated analytics that continuously monitor for fraudulent indicators. The new offerings feature advanced analytics that understand non-obvious relationships and co-occurrences between entities, new enhanced visualization technologies that can identify and connect fraudulent patterns closer to point of operation, and machine learning to help prevent future occurrence based on previous attacks and behaviors.