Fraud and financial crimes cause $3.5 trillion in losses each year, according to IBM. To help businesses stem those losses, IBM has introduced new software and services to help organizations use big data as part of their efforts to prevent, identify and investigate fraudulent activities.
IBM's new "Smarter counter fraud" initiative draws on 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 includes new software that, according to IBM, "allows organizations to gain better visibility and take a more proactive holistic approach to countering fraud, through the aggregation of Big Data across a variety of internal and external sources -- including mobile, social and online -- and the application of sophisticated analytics to continuously monitor for fraudulent indicators. This includes advanced analytics that understand non-obvious relationships and co-occurences 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."
The goal is to address all kinds of fraud and financial crimes, including money laundering and cyber-attacks, to insider threats, to tax evasion. According to IBM, "The new offerings can detect cross-channel mobile fraud and prevent cybercrime enablers like phishing scams. They can enable an insurance company to review thousands of claims in real-time to flag potentially fraudulent activity while processing legitimate claims faster. Or help a global bank more accurately detect and investigate money laundering activities to meet regulatory compliance."
The new software and services include:
Counter Fraud Management Software,, which brings together IBM's big data and analytics capabilities to help organizations aggregate data from external and internal sources and apply sophisticated analytics to prevent, identify and investigate suspicious activity. It includes analytics that understand non-obvious relationships between entities, visualization technology that identifies larger patterns of fraud, and machine learning to help prevent future occurrence based on previous attacks.