We’ve seen an explosion in digital commerce and online banking over the last few years, as people spend more time on the Internet, and complete transactions from a wider range of devices, including mobile phones and tablets. Although the growth in transactions is good news for banks, retailers and other online service providers, it also means a correspondingly high rate of growth in digital fraud.
The challenge for fraud management teams is to develop ways to distinguish good transactions from fraudulent ones, without impacting negatively on genuine customers. Analysis of large volumes of data to identify the behavioral patterns associated with genuine and fraudulent activity—such as setting up of new accounts—is essential for businesses wishing to improve the accuracy of fraud risk assessments.
Distinguish good transactions from bad
The use of machine learning and behavior analytics is increasingly being used in advanced fraud detection technologies to evaluate transactional risk, especially in banking and payments. The self-learning capabilities enabled by machine learning allow risk models to adjust quickly to new threats when new patterns of fraud are uncovered.
Our data science team recently evaluated a series of fraud patterns across a number of use cases as identified by the RSA Risk Engine, including the relationship between fraud, new accounts, and device. Highlights of the analysis showed:
- Fraud is fifteen times more likely to originate from a new account than from one that has been established for over thirty days. The probability of fraud drops dramatically for new accounts after (Read more...)
*** This is a Security Bloggers Network syndicated blog from RSA Blog authored by Heidi Bleau. Read the original post at: http://www.rsa.com/en-us/blog/2017-11/new-account-fraud-how-to-apply-fraud-data-to-reduce-risk.html