Fremont, CA: Since consumers use more online and mobile banking channels for their monetary operations, due to the pandemic and decreased in-person touchpoints, hackers perceive this change as a chance to take advantage of the vulnerabilities across these same bank channels. As a result, new fraud attacks are on the rise.
Banks may gain greater insight and analyze risk in real-time by shifting away from legacy technologies and embracing modern artificial intelligence and machine learning in a unified fashion across their mobile and internet platforms. Here are three ways for improving digital banking channels:
Facial recognition as part of identity verification
During the pandemic, fraudsters are also using application fraud to take advantage of users. By establishing digital identity verification checks as the first line of defense, banks can prevent this type of fraud and notice when hackers attempt to utilize synthetic identities across digital channels. The use of ID document verification with facial comparison is one of the most effective approaches. A customer can simply scan a government-issued ID using their smartphone camera and then take a photo. Biometric face comparison methods with liveness detection ensure that the ID is genuine and unaltered and that the person opening the account is the same person as the ID photo.
E-signatures to enable secure remote transactions
Banks can use e-signatures as one of the technologies to increase the speed and convenience of their business processes right away. Banks and financial services professionals can speed up in minutes with a web or mobile e-sign tool, ensuring that agreements are enacted the same day they are requested. Furthermore, by combining digital identity verification technology with e-sign solutions, financial institutions can continue to supply critical services like digital mortgage lending and remote online notarization for home purchases while the epidemic is ongoing.
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Preventing account takeover attacks with risk analytics
Most banks and financial institutions have pre-configured criteria for detecting known fraud, but the pace and volume of today's attacks render these rules obsolete, as they are not designed to guard against evolving fraud techniques. Banks and financial institutions can use machine learning to discover aberrant patterns that indicate fresh fraud assaults in real-time by combining their set of pre-configured rules with advanced risk analytics engines.
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