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Outsmarting Fraudsters With Cognitive Fraud Detection

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Can your financial institution's fraud detection system learn, reason and adapt to new and emerging cyberthreats? Can it identify fraudulent behavior within your account simply by analyzing interactions and patterns? In this day and age, people can access their bank accounts anywhere, anytime. We need strong, agile and efficient fraud detection systems to keep financial institutions and their customers safe. Mobile functionality and safety are among customers' top concerns when it comes to online banking -- so IBM Security Trusteer is releasing new cognitive fraud detection and behavioral biometric functionality that accomplishes just that.


How Artificial Intelligence is Helping Financial Firms in the Fight Against Fraud

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Onfido's 2022 Identify Fraud Report has identified a concerning 47 per cent increase in identity fraud since 2019, with financial services remaining one of the highest targeted sectors. Further research from McAfee reveals cybercrime costs the global economy $600billion annually, while consulting firm Accenture forecasts cyberattacks could cost companies $5.2trillion worldwide by 2024. Global payment card fraud losses, specifically, amounted to $28.58billion in 2020, says a Nilson report. Payments card fraud is such a concern that the UK recently implemented tighter anti-fraud checks on card payments with new Strong Customer Authentication (SCA) rules coming into force in March 2022, activated for almost all online purchases above £25 to provide a greater level of security against fraudsters. With cybercriminals getting ever more inventive with their malicious get-rich-quick schemes, fraud prevention and detection has never been more critical.


How US Bank Uses Machine Learning To Fight Fraud PYMNTS.com

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Mobile and online banking providers have been upping their fraud protection measures over the last decade, making it more difficult for bad actors to rely on some of the schemes that previously worked in such channels. The prevalence of CNP fraud, once the bread and butter of the enterprising cybercriminal, has steadily crept downward each year alongside other forms that game customers' credit card numbers. Cybercriminals are still masters of a thriving trade, though. Banks are dealing with rapid rises in fraud schemes such as ATOs, synthetic identity fraud and account opening fraud. Creating new credit or mobile device accounts is a popular application, which uses legitimate customers' stolen information to defraud both them and their financial institutions (FIs).


Cross-channel fraud detection

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Cyber fraud costs organizations billions of dollars each year, and its financial impact continues to climb as criminals are getting smarter and their attacks more complex. While the increasing need for rapid and complex fraud risk detection is common in many sectors, it is perhaps most acute among financial institutions and online merchants. Competition is fierce in these highly digitized markets, and margins are razor-thin. Customers are extremely demanding, and constantly seek better, more user-friendly payment options and channels. Cross-channel fraud detection has been an area of focus for both business and security leaders for nearly a decade.


Machine learning is the best tie to bind user experience and security

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Recent news about data breaches has consumers on high alert, and, rightly so. Hacked data is one of the many tools criminals use to facilitate fraudulent transactions. According to some reports, U.S. banks and merchants will incur more than $12 billion in fraud loses by 2020, up from $8.45 billion in 2015.