Fighting Fraud in the Age of Big Data
Latest posts by Priya Rajan (see all)
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Since the introduction of the web in the early 1990s, public’s expectations of how personal information is being used has been altered, creating new opportunities for business. According to a 2016 Pew Research Poll, most Americans are willing to give out personal information or allow their own actions to be monitored if they feel they are getting something in return. The subjects in the study were provided with a number of different potential trade-offs between privacy and benefits. For instance, 52 percent said that they would upload their medical records to a website if it would make scheduling appointments and viewing those records easier.
These results indicate how the internet has transformed the nature of privacy and opened individuals up to allowing unprecedented access to their personal information. However this sharing of information is not without concerns. In the Harris Interactive survey of 2,143 U.S. adults conducted in April, 42 percent of respondents “are most afraid of their Social Security numbers being stolen by a hacker.” The No. 2 fear is related to banking login data being compromised (28 percent), followed by credit card information (15 percent). In spite of lingering concerns about security and exposure, millions of people regularly share the details of their lives and vital data through online profiles. They do so because of the overwhelming benefits of convenience and connection over potential risks.
The journey to omnidata
As the Internet Society recounted, the internet and its precursor ARPAnet served as a research tool to pass along data and communications between relatively few users on shared machines for military and academic purposes. Since then, the internet has transformed into one of the greatest tools for communication, business and research in human history. Along the way, it has altered the way people view and manage their own privacy, This shift is apparent from the ways we use use email, search and online retail.
Email brought the major advantages of near-instant communication for both personal and business uses. However, it also led to the scourge of spam, including unsolicited advertising and scams. The Privacy Rights Clearinghouse pointed out that many email providers automatically scan content to remove spam from inboxes. Account holders allow access to to often highly personal or confidential messages because it means an improved overall experience. According to TechCrunch, Gmail had over 1 billion users active on a monthly basis by February 2016, most accessing the application on mobile devices.
In some cases, the providers in turn use keywords in your messages to target advertising or handy reminders, like updates on a scheduled flight or travel times to a concert. A great many users have accepted this exchange of privacy for personalization.
Embracing the cashless economy
Thanks to shifts in the public’s attitude toward privacy, businesses can take advantage of the abundance of information circulated online.
According to the Federal Reserve, 53 percent of those who owned a smartphone and held a bank account used mobile banking in 2015. Moreover, Pew found that 65 percent of American adults use social networking websites like Facebook, Twitter and Instagram. Companies use analytics to accumulate information shared through a variety of online interactions to develop behavioral marketing strategies. This approach offers powerful tools to learn more about customers and efficiently identify normal behavior patterns. As CBS News discussed, organizations can acquire a clear picture of every step that an individual takes leading to a purchase decision or another engagement, such as viewing a video or downloading a piece of literature.
This understanding of customer behavior is not beneficial for targeting good customers but also particularly advantageous when it comes to loss prevention. As companies collect more and more data about its customers, using computational power to categorize the data into recognizable patterns can help identify characteristics of desirable behavior from fraudulent ones.
By using advanced machine learning for fraud detection, companies can identify deviations from the normal behavior using segments that are based on hundreds of data points, making detection more efficient and accurate. Machine learning combined with human intelligence can transform big data into actionable insights to identify and prevent fraud in real time without compromising user experience.
To learn more about how machine learning applies to fraud prevention, download the white paper “A Primer To Machine Learning For Fraud Prevention“.