Cybercrime is expected to cost the world $10.5 trillion annually by 2025, but machine learning is helping merchants and financial institutions combat criminal activity.
Monica Eaton CEO Chargeback 911, An international chargeback management and prevention company offering SaaS solutions for chargeback management explains why artificial intelligence (AI) has always been at the forefront of fraud prevention.
The emergence of generative artificial intelligence has garnered a lot of attention over the years, with many of the companies behind the rise of AI applications seeing skyrocketing valuations, but the technology is not unfamiliar territory in the financial industry, particularly in the area of ​​chargebacks.
Machine learning (ML) solutions were introduced years ago to aggregate and segment large amounts of transactional data to support policy, operations, and decision-making for banks and corporations.
This technology is especially important today, when manually combating online fraud and chargeback abuse is nearly impossible, especially with cybercrime as a whole predicted to cost the world $10.5 trillion per year by 2025.
While everyone is talking about AI and its overall potential, I aim to answer what AI is, what it can do, and what it has done to keep stakeholders safe over the years.
A Close-Up on AI
As depicted in the movies, AI is simply a virtual entity with human-like intelligence. This emerging technology is being trusted enough to converse, ask questions, and solve problems in real time without human oversight.
but, OpenAI, Google It is very different from what others have created: ChatGPT can only complete certain tasks based solely on constructed information, whereas the human brain performs tasks based on distinct perspectives, opinions, or personalities.
Large-scale language models (LLMs) like ChatGPT can produce an unlimited number of accurate, well-written content, similar to the autocorrect feature on your phone. By learning what words will follow a given question and accurately predicting the answer, LLMs can make a compelling impression as living, responsive beings. However, they can fall short when they don't understand the meaning or are operating with the limited context behind those words and questions.
With a large enough dataset and enough tuning by a human programmer, LLMs can produce highly realistic, human-like interactions, but programmers and users should be careful that AI tools can make mistakes, confuse, or mislead if the information on which they base their responses is inaccurate or out of date.
Leverage AI to prevent fraud and reduce chargebacks
AI is prone to error, so how do you mitigate the risks when using it to fight fraud? While you need to ensure your AI tools are operating within the proper bounds and are accurate and up to date, AI (or more accurately ML) in fraud prevention applications has gotten better at detecting fraud and presenting chargebacks over time.
The fraud prevention industry can quickly spot irregularities and patterns in data, something that computers are particularly good at. For example, if all the fields on an order form are filled out in an instant, whereas most humans would take a little longer, it could be an indication that the form is being filled out by an automated rather than a human, a clear sign of fraudulent activity. Another example is when shipping and billing addresses are too far apart, AI can automatically flag the transaction for inquiry.
ML can effectively detect irregularities in chargeback management, even if they are just issuing chargeback claims too frequently. As it is also important to complete the task for each retailer, machine learning algorithms learn the specific nuances of how fraudulent chargebacks impact a specific merchant's business. They can quickly learn the telltale signs of chargebacks (both valid and invalid) with faster connections than humans can. This increases customer satisfaction as only legitimate transactions are efficiently passed through.
Trusted and mature technology for retail and fraud prevention
Using AI to prevent fraud and chargebacks will inevitably involve trial and error and learning opportunities along the way, but the technology is maturing as retailers around the world trust it to provide more reliable data on which to base their decisions. To put AI to good use, retailers need to be realistic about its capabilities as more retailers introduce it into their workflows over the next few years.
