Stay ahead of cyberattacks and fraud with predictive analytics
Even as cyber criminals and swindlers step up their game, companies can use predictive analytics to stay ahead. Discover the full scope of IBM SPSS predictive analytics capabilities.
By IBM Sponsored Post.
Fraud is big business. Indeed, many companies expect to lose roughly 5 percent of revenue to fraud each year—and even worse than the monetary loss that fraud incurs is fraud’s toll on customer trust and a company’s reputation. But even as cyber criminals and swindlers step up their game, companies can use predictive analytics to stay a step ahead—or two.
Uncover the signs of fraud in your data
Despite their resourcefulness, fraudsters nonetheless leave behind a trail of behavioral and transactional data, and such data contains anomalies that can help betray fraudulent activity to a watchful eye. However, because the patterns that reveal fraudulent activity generally hide among massive quantities of data, a proactive approach to fraud management must begin with a framework for analyzing both a company’s massive data stores and its up-to-the-minute transactional data, as noted in the recent Ventana Research benchmark Next-generation Predictive Analytics: Using Forward-looking Insights to Gain Competitive Advantage.
Armed with advanced analytics capabilities, companies can detect subtle patterns and associations in data for use in building predictive models. Such models incorporate data from both numeric (structured) data and textual (unstructured) data records, whether emails, social media interactions, call center notes or agents’ reports. In contrast with rules-based detection models, predictive models continually “learn” from data, tracking shifting patterns to detect new scams even as they emerge.
Though no business is immune to fraud loss, certain industries—such as the insurance sector—by their nature attract fraudsters disproportionately. In the United States, for example, insurance fraud—excluding health insurance fraud—incurs an estimated $40 billion in costs every year, boosting premiums across the board. As companies struggle to cut costs by mitigating the effects of fraud, predictive analytics algorithms scrutinize claims in a multistage process designed to help insurance companies efficiently detect and eliminate fraudulent activity by revealing insights into fraudulent patterns and claims data.
Adopt a culture of analytics
By implementing IBM SPSS predictive analytics solutions, the Infinity Property and Casualty Corporation of Birmingham, Alabama, gained the ability to closely scrutinize claims histories, flagging suspicious claims for further investigation while fast-tracking legitimate claims. As a result, the company saw a 400 percent return on investment within six months of implementing its system, adding $1 million to its bottom line and cutting by 95 percent the time required to refer a questionable claim for investigation. These effects on the company’s health were further emphasized by its streamlined ability to provide customer payouts—a breakthrough differentiator in an increasingly commoditized business.
Predictive analytics provides organizations with a distinct edge by injecting new intelligence and insight into operations. What’s more, it helps drastically reduce losses incurred by fraud even while streamlining operations. By doing so, it can help a company differentiate itself from others that have not yet adopted a culture of analytics.