Improving technologies promise deterrents to credit card fraud, However, the stakes are very high and it is a never-ending battle since hackers are not only knowledgeable in the very same techniques but innovate their methodologies very frequently. Compromised credit card information has led to e-commerce companies, banks and credit card companies needing highly technical software solutions to avoid losing billions of dollars.
Among the very many solutions offered the ones based on technology handling the credit card’s big data analytics courses is the safest bet. The adage prevention is better than cure applies well here and smart techniques in ML with Big data systems can counter such frauds.
The typical process:
Financial institutions that issue credit cards or companies that offer credit-facilities usually create an exhaustive user profile of their customers. Details indicated here are the user mobile number, call center conversations, social media accounts, data from customer’s devices and more helping the analytics systems collect and use information from multiple origins and sources. The AI behind the scene analyses typical customer behavior and involves very large big data analytics courses and Big Data sets across disparate sources.
A red flag is raised if any unusual or deviation from normal buying patterns is observed. The customer will then receive an immediate alert seeking information if such transaction was indeed made by them.
Previously phone calls were made. But currently, automated messages serve the purposes of both privacy and personalization.
Typical red flag scenarios pointing to illegal and shady activities that immediately arouse suspicions are:
- Use of a new unrecorded device for credit card transactions.
- Several rapid transactions happening using different devices in a short span of time all on the same day.
- Multiple transactions on the credit card occurring at far-off different locales and cities within a few minutes or hours of each other.
- The transacting amount is higher than the expected monthly spending amounts and the pattern of buying behavior.
- The card is used suddenly for high-volume and large purchases.
Constant vigil by the system flags unusual parameters and behavior in their trend analysis. If the transaction is red-flagged the clients are asked to verify purchases.
Since the credit card segment is all about customers and safe use of their cards, the personalization features of the software enhance their user experiences and also allows them to track their own purchase history. For example, credit cardholders can choose to limit their transactions to a particular limit and be alerted if the limit is exceeded. They can also enable authentication requirements for each transaction by simply opting for alerts on their profile page setting off triggers in the big data analytics courses behind their cards and profile.
Automated fraud detection is more complicated than you imagined. And blocking credit cards can be a double-edged sword for both the client and financial institution. Imagine scenarios of the card being used at a different or foreign locale.
Normally blocking the card is an option that needs caution and should be used when the cardholder has not informed the bank of his/her plans and most importantly the mobile user number, social media posts and location does not match the transactional details to avoid a false-positive result and inconvenience to the client. More complications arise for changes in buying behavior of customers which impact the financial companies as legitimate purchases getting flagged are higher.
Thus, to avoid customer inconvenience the fraud detection techniques need constant overhauling and innovation with the latest current data and ensuring the AI is constantly learning by being fed fresh data.
Data collection is the foundational basis of fraud detection. One must check the data privacy rules applicable to that particular country and locale before gathering data for clients. New data and new techniques of fraud detection should be applied continuously to stay ahead of the fraudsters who use the data for wrongful means and are experts who cash in knowing the lacunae of data analysis in the credit card segment.