A report by nbcnews.com has shared that Visa is currently “monitoring 76,000 transactions a second across more than 200 countries, facing off against hackers and fraudsters determined to steal money. The technology is powered by artificial intelligence”.
The focus for a while now has shifted to count on AI and algorithms, as opposed to relying heavily on rules-based solutions, to spot anomaly in what don’t come across as regular transactions as well as user behaviour in real-time. Another aspect is to ensure that legitimate shoppers don’t get blocked. Also, algorithms keep on improving as they count on varied transactional patterns and behaviour, leveraging large datasets to separate authentic payments from fraudulent ones.
AI can detect this type of fraud because it doesn’t solely rely on IPs and IP reputation to stop incoming threats. AI monitors user behavior to distinguish bots from people and block malicious bots. In the rare case of uncertainty, it presents the user with a CAPTCHA.
The more data you feed a machine learning engine, the more trained it becomes, but it also needs to be remembered that incorrect data results in poor decision making. The efficacy needs to be checked for algorithms’ performance in terms of separating genuine and friendly fraud transactions, as well as the number of false declines. Also, evaluate the pros and cons of blackbox machine learning and whitebox machine learning.
Keen on knowing how to rely on AI to combat credit card fraud?
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By Ritesh Gupta, Ai Events
