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April 7, 2021 - Claire Zhou

Bracing for Impact: The Future of Digital Banking Fraud Prevention

Financial institutions are making an inexorable shift toward digital transformation. This shift not only calls into question the customer experience but also the way in which banks detect and prevent fraud. 

Consumer expectations are largely responsible for this push. They expect seamless omnichannel banking that works around their schedule. Deloitte believes that for the bank of the future to win, it will need to “embrace emerging technology, remain flexible to adopt evolving business models, and put customers at the center of every strategy.” 

As consumers become more reliant on digital platforms for financial services, fraud prevention techniques have to adapt. In terms of fraud detection and prevention, here’s what banks should consider as they innovate to better serve their customers, stakeholders, and employees. 

Device Intelligence to Detect Mobile Fraud

Digital banking includes the use of mobile devices to perform banking activities, from checking account balances via app to connecting with bank representatives to filling out loan applications. Device intelligence will be essential in detecting and preventing attacks from manipulated devices. For example, multiple requests that appear different but come from the same device may indicate a sophisticated crime ring, prompting banks to investigate further.

DataVisor’s Device Intelligence collects device data at scale and in real time to detect compromised devices, including emulators, repackaged apps, and rooted devices. Fast-evolving mobile attacks can be detected and prevented, even when fraudsters change their device IDs to obscure their activities. 

Machine Learning for Real-Time Fraud Detection

Part of what makes modern fraud detection and prevention so tricky is that fraud is quickly evolving. Even with supervised machine learning, many fraud models are adjusted based on previously detected activities, which means fraud teams are continually playing catch-up to criminals. 

Maintaining a competitive edge, especially as banks embrace more technologies, requires a real-time strategy that can stop losses in their tracks rather than trying to do damage control after the fact. Centralized data intelligence enhances this process by allowing FIs to create features and models for a variety of use cases in a matter of minutes. DataVisor’s use of unsupervised machine learning doesn’t rely on only known issues and can help fraud teams evolve and adapt in real time. FIs can make better predictions and data-driven decisions to gain a competitive edge.

Fraud Prevention and Detection that Improve the Customer Experience

Part of the move toward digital banking is the assumed advantage of fast, seamless experiences. These can only be achieved, however, when fraud prevention measures do not get in the way of good customers conducting business. 

Finding ways to improve real-time fraud detection accuracy while minimizing friction for authentic transactions will be essential for customer retention. What’s more, doing so can also earn customer trust in knowing their accounts and personal information are protected. Both of these can be accomplished with device intelligent and global intelligence networks that deliver high accuracy with low false positives. As consumers are increasingly digital, FIs should seek agile yet impactful solutions that can help them keep pace with their customers’ expectations. Want to see one such solution in action? Experience proactive AI-powered fraud prevention today. 

Photo of Claire Zhou
about Claire Zhou
Claire is a Senior Product Marketing Manager at DataVisor with over 5 years of marketing experience in security and fin-tech. She is passionate about empowering enterprise customers with AI-based solutions. Her expertise spans data analytics, cybersecurity, and fraud prevention. Claire has an MBA from UCLA.
Photo of Claire Zhou
about Claire Zhou
Claire is a Senior Product Marketing Manager at DataVisor with over 5 years of marketing experience in security and fin-tech. She is passionate about empowering enterprise customers with AI-based solutions. Her expertise spans data analytics, cybersecurity, and fraud prevention. Claire has an MBA from UCLA.