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 losses by 2020, up from $8.45 billion in 2015.
As consumers continue to demand a faster, easier checkout experience, there is an increased opportunity for fraudulent activity with reduced consumer authentication and verification. Never before has it been so critical for processors, issuers and merchants to deliver effective solutions in a highly secure environment.
New hardware and software solutions aim to combat these attacks. Unfortunately, some new technology offerings have simply steered fraudsters to different channels.
For example, the EMV rollout in the U.S. had its share of challenges, including inconsistent customer experiences, which led to longer transaction times and frustrated consumers. In some instances, immediately following the rollout, consumers actually left their cards behind in the POS devices, which created even more lost/stolen cases.
As the industry evolves and introduces new technology, we have not yet seen the silver bullet that prevents all fraud attacks. Enter adaptive behavioral analytics. Ten years ago, this capability was viewed as the bright and shiny object with unparalleled possibilities. Yet, few organizations knew how to successfully leverage machine learning.
Financial institutions tend to be slow to adopt technology, contrary to fraudsters, who are becoming even more sophisticated in their approaches. Financial institutions have to enhance their collective technology offering to improve fraud detection and prevention efforts.
Machine learning can stand alone but is most useful when complementing a broader, strategic fraud prevention approach. The right platform can detect unusual behaviors and block fraudulent transactions as they occur, while still allowing real customers to make legitimate transactions without interruptions.
Even with all the advantages machine learning offers, it is really only as good as the data scientists who are building it and the data they obtain for decisioning purposes. In some instances, data scientists have built machine learning systems that worked well in the lab but were ill-equipped to handle the complexities that come with real-time, real-world, enterprise use cases.
The “garbage in, garbage out” concept is one of the guiding principles when it comes to machine learning platforms. The technology itself isn’t an all-encompassing solution for any business or consumer challenge, including fraud. Yet, it can aid financial institutions in creating strategies that drive better customer outcomes.
For example, machine learning’s ability to process data in milliseconds protects consumers in a way that doesn’t diminish their collective payment experience. This in turn reduces operational costs for merchants, processors and financial institutions and provides a simpler, more secure customer experience.
Machine learning technology has existed for more than 30 years and is being actively used to uncover customer sentiment down to what books they order online or what music they enjoy. For businesses relying on repeat customers, this has become a critical level of service in the daily lives of their customers.
However, apart from personalizing the customer experience in an e-commerce environment, machine learning has also become an incredibly useful tool in fighting fraud. And while customers continue to enjoy shopping recommendations based off their profiles, machine learning’s role as fraud fighter may be its greatest role yet.