Financial fraud is growing rapidly—the ability to process data quickly and identify patterns with AI and machine learning can help your fraud detection programs meet new challenges.
Fraud and other cybercrimes are an ongoing threat, and the situation is getting worse. PwC’s 2020 Global Economic Crime and Fraud survey found that 47% of companies had experienced fraud in the past two years at an estimated total cost of $42 billion.
As online banking usage grows, fraud has also expanded. More than a third (35%) of retail banking customers increased their use of online banking during the Covid-19 pandemic, and it’s safe to assume that this could become a new normal. As the payments industry continues optimizing transactions for maximum speed, fraud detection platforms have even less time to react.
Given the high costs associated with remediating financial fraud after the event, companies are working hard to improve their ability to detect and prevent fraud from occurring. KYC and anti-money laundering measures have long played a role in detecting fraud, but criminals are constantly devising new ways to game the system. Companies that cannot deploy the latest tools to stay ahead of malicious actors may find themselves targeted more often.
The industry clearly recognizes it has a problem, and financial services companies are investing in advanced tools to solve it. Forrester predicts that enterprise spending on cloud security tools, for example, will reach $12.6 billion by 2023, up from $5.6 billion in 2018. But what tools should they invest in, and what trends should theyfocus on? This white paper examines the latest trends in financial fraud, suggests areas where companies can fight back effectively, and explains how Redis is helping businesses achieve these goals
