AI has ensured that companies are well-equipped to take on the kind of nuanced, highly sophisticated fraud now being carried out around the world. It quickly alerts analysts to anomalies, develops trend-based insights, and discerns if a given transaction or series of financial activities are unusual or fraudulent. By applying ML, companies can mine historical and live data to locate patterns within customers’ behaviour, and can then evaluate every transaction to make accurate fraud predictions. The more data that’s collected across historical transactions, the better the precision in fraud detection.
The pros and cons of AI and ML in DevOps
AI and ML are now common within most digital processes, but they bring faults as well as benefits when it comes to DevOps.
When a breach or fraud is detected, AI also allows businesses to move quickly to address challenges and swiftly problem-solve. Let’s say that fraudsters get creative and tailor their attacks to news events — for instance, what we’ve been seeing in the news recently with personal protective equipment (PPE)-selling scams. As these new types of fraudulent transactions are identified, the AI model will automatically adapt based on the pattern of these transactions. This is a contrast to the legacy way of managing fraud, which was reliant upon a person to trial and error new business rules to detect fraudulent transactions.
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Especially during Covid-19, virtual cards have the ability to significantly reduce the risk of fraud. They can be set up to be used only once, so even if the data is subject to a breach, the card cannot be used if the payment has been processed. Additionally, single-use virtual cards typically have tight controls associated with them, such as the amount (which can be a range of amounts or one specific amount), expiration date, merchant, and even Merchant Category Codes, among other parameters. With regard to amounts, single-use virtual cards can be authorised to use with one exact amount only, or they can be authorised for multiple transactions that ultimately result in that one exact amount.