Development in Fraud Management for Credit Card 2021

Update Rana Gohil

Development in Fraud Management for Credit Card 2021

 Hi friends, 

You can see New management for credit card 2021. All info update. Apart from technological advances, another trend which has emerged during the recent years is that fraud prevention is moving from back-office transaction processing systems to front-office authorization systems to prevent committing of potentially fraudulent transactions.  


The technology for detecting credit card frauds is advancing at a rapid pace – rules based systems, neural networks, chip cards and bio metrics are some of the popular techniques employed by Issuing and Acquiring banks these days.  However, this is a challenging trade-off between the response time for processing an authorization request and extent of screening that should be carried out.


Risk scoring tools are based on statistical models designed to recognize fraudulent Transactions, based on a number of indicators derived from the transaction characteristics. Typically, these tools generate a numeric score indicating the likelihood of a transaction being fraudulent: The higher the score, the more suspicious the order. Risk scoring systems provide one of the most effective fraud prevention tools available. The primary advantage of risk scoring is the comprehensive evaluation of a transaction being captured by a single number.

While individual fraud rules typically evaluate a few simultaneous conditions, a risk-scoring system arrives at the final score by weighting several dozens of fraud indicators, derived from the current transaction attributes as well as cardholder historical activities. E.g., transaction amounts more than three times the average transaction amount for the cardholder in the last one year. 

The second advantage of risk scoring is that, while a fraud rule would either flag or not flag a transaction, the actual score indicates the degree of suspicion on each transaction. Thus, transactions can be prioritized based on the risk score and given a limited capacity for manual review, only those with the highest score would be reviewed.


Neural networks are an extension of risk scoring techniques. They are based on the ‘statistical knowledge’ contained in extensive databases of historical transactions, and fraudulent ones in particular. 

These neural network models are basically ‘trained’ by using examples of both legitimate and fraudulent transactions and are able to correlate and weigh various fraud indicators (e.g., unusual transaction amount, card history, etc) to the occurrence of fraud. A neural network is a computerized system that sorts data logically by performing the

Following tasks:

👉 Identifies cardholders buying and fraudulent activity patterns.

👉 Processes data by trial and elimination (excluding data that is not relevant to the pattern).

👉 Finds relationships in the patterns and current transaction data.

The advantages neural networks offer over other techniques are that these models are able to learn from the past and thus, improve results as time passes. The principles of neural networking are motivated by the functions of the brain – especially pattern recognition and associative memory. The neural network recognizes similar patterns, predicting future values or events based upon the associative memory of the patterns it has learned.  They can also extract rules and predict future activity based on the current situation. By employing neural networks effectively, banks can detect fraudulent use of a card, faster and more efficiently.


Bio metrics is the name given to a fraud prevention technique that records a unique characteristic of the cardholder like, a fingerprint or how he/she sign his/her name, so that it can be read by a computer. The computer can then compare the stored characteristic with that of the person presenting the card to make sure that the right person has the right card. Bio metrics, which provides a means to identify an individual through the verification of unique physical or behavioral characteristics, seems to supersede PIN as a basis for the next generation of personal identity verification systems. 

There are many types of bio metrics systems under development such as finger print verification, hand based verification, retinal and iris scanning and dynamic signature verification.


Smart credit cards operate in the same way as their magnetic counterparts, the only difference being that an electronic chip is embedded in the card. These smart chips add extra security to the card. Smart credit cards contain 32-kilobyte microprocessors, which is capable of generating 72 quadrillion or more possible encryption keys and thus making it practically impossible to fraudulently decode information in the chip. The smart chip has made credit cards a lot more secure; however, the technology is still being run alongside the magnetic strip technology due to a slow uptake of smart card reading terminals in the world market. 

Smart cards have evolved significantly over the past decade and offer several advantages compared to a general-purpose magnetic stripe card. A consortium of Europay MasterCard and Visa (EMV) recently issued a set of specifications for embedding chips in credit cards and processing transactions from such cards. MasterCard and Visa have also issued deadlines for compliance with these specifications indicating that banks will have to bear a large portion of fraud losses if they do not comply with EMV specifications. However, the market response has been slow so far due to large investments needed in implementing the EMV compliant programs.


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