1. Paper checks will disappear. Covid-19 and the urgent need to reduce the spread of infection have accelerated the adoption of digital payment methods, eliminating the need for paper checks, both for consumers and businesses. In fact, the number of paper checks has dropped roughly 1.8 billion a year, and at this pace, they’re likely to disappear completely. As paper checks are replaced by digital payment methods, digital fraud associated with cash apps, card-not-present (CNP) transactions and wireless transfers will increase, requiring proactive, adaptive detection and prevention.
2. Proactive fraud solutions will replace legacy, reactive solutions. Innovations in the online payment industry will lead to the development of new fraud attacks, as fraudsters adapt their methods. Unfortunately, legacy fraud solutions require fraud teams to have significant experience and access to historical data, neither of which exist for these new digital payment options. Companies will need to invest significantly in proactive cybersecurity protection that feature advanced machine learning such as unsupervised machine learning (UML) to defend these new digital apps and channels.
3. Fraud detection will be instantaneous. Modern attacks are organized, sophisticated, and at a large scale — fraudsters use botnets or emulators to launch waves of attacks at scale to create accounts, transfer funds, make payments, or abuse promotions. With the payment industry shifting toward real time instantaneous settlement, a slow response can lead to enormous fraud losses. To get ahead of fraudsters and reduce legitimate user frictions, organizations will adopt real-time detection technologies and do away with batch systems or extensive human reviews that are delayed in reaction.
** 4. Mobile fraud will continue to increase, making AI-powered device intelligence essential**. Mobile fraud has grown at least 44% year over year, and last year, 93% of the total mobile transactions in 20 countries were blocked as fraudulent. Fraudsters may create malicious apps that appear legitimate, to get a user to download it to their device. They can run install farms — physical banks of mobile phones or use device emulators to run bot-based scripts and pose as real end-users to commit application fraud. Because fraudsters can manipulate data at multiple touchpoints throughout the user journey, it’s important to collect data at the source, to validate whether the user is legitimate. To that end, AI-powered device intelligence will become a critical feature of fraud and risk solutions, helping to stop mobile fraud before it causes damage .