Credit card fraud is one of the major problems in the financial industry today. More transactions
have dramatically increased the cases of credit card fraud, causing a huge amount of financial losses
to card issuers and other businesses. Because of this, many credit card companies are now
employing various solutions that can help reduce these problems.
One of the primary solutions employed by the financial institutions and businesses is adopting a
credit card fraud detection method. Fraud detection methods are designed to spot possible
fraudulent activity before it can cause any damage.
Below is a list of the most commonly used credit card fraud detection methods;
Clustering Techniques
The Peer Group Analysis System is a type of clustering technique used to detect possible credit card
fraud. This system involves grouping credit card accounts that follow similar behavioral pattern. Any
erratic change in behavior on any of the accounts can raise a red flag, which would require the
intervention of a fraud analyst. The fraud analyst can then start their investigation by notifying the
account owner regarding the irregularity. Sudden changes in the volume of transactions or an
unusually high amount transfered can be easily spotted through this fraud detection technique.
Bayesian Networks
Bayesian Networks fraud detection method employs two models of behaviour, which help to
determine possible credit card fraud. These two models are called the Fraudulent and the Non-
Fraudulent models. The Fraudulent Model is constructed the way a fraudulent user would behave,
which is based on a fraud expert’s opinion. The Non-fraudulent model on the other hand is modelled
based on how a legitimate credit card owner behaves. These models are then used to logically
evaluate the credit card transactions of every user.
Decision Tree
Decision Tree fraud detection technique utilizes data mining to determine patterns that it can use to
set up its logical attributes. These logical attributes are used to come up with classification rules,
where each credit card transaction will be evaluated through IF and THEN tests. The transaction will
have to successfully satisfy these tests to be classified as a legit transaction. Credit card transactions
that fail to satisfy the mapped expected behavior are flagged as fraudulent and require the attention
of the card owner for further validation.
Hidden Markov Model
This type of credit card fraud detection technique utilizes the habitsof the credit card user to create
acceptable parameters for non- fraudulent transactions. A history ofcredit card transactions
combined with the cardholders new operations will help the system compute for probabilities. If the
probabilities are within the acceptable parameters set by the system, then the transaction is
considered legit, otherwise the attention of the card owner will be called for a suspicious credit card
operation.
These techniques are just some of the many credit card fraud detection methods that are employed
by various businesses and financial institutions. To date, credit card companies are continuously
developing better fraud detection methods to help them keep up with the new credit card fraud
threats in the industry.
have dramatically increased the cases of credit card fraud, causing a huge amount of financial losses
to card issuers and other businesses. Because of this, many credit card companies are now
employing various solutions that can help reduce these problems.
One of the primary solutions employed by the financial institutions and businesses is adopting a
credit card fraud detection method. Fraud detection methods are designed to spot possible
fraudulent activity before it can cause any damage.
Below is a list of the most commonly used credit card fraud detection methods;
Clustering Techniques
The Peer Group Analysis System is a type of clustering technique used to detect possible credit card
fraud. This system involves grouping credit card accounts that follow similar behavioral pattern. Any
erratic change in behavior on any of the accounts can raise a red flag, which would require the
intervention of a fraud analyst. The fraud analyst can then start their investigation by notifying the
account owner regarding the irregularity. Sudden changes in the volume of transactions or an
unusually high amount transfered can be easily spotted through this fraud detection technique.
Bayesian Networks
Bayesian Networks fraud detection method employs two models of behaviour, which help to
determine possible credit card fraud. These two models are called the Fraudulent and the Non-
Fraudulent models. The Fraudulent Model is constructed the way a fraudulent user would behave,
which is based on a fraud expert’s opinion. The Non-fraudulent model on the other hand is modelled
based on how a legitimate credit card owner behaves. These models are then used to logically
evaluate the credit card transactions of every user.
Decision Tree
Decision Tree fraud detection technique utilizes data mining to determine patterns that it can use to
set up its logical attributes. These logical attributes are used to come up with classification rules,
where each credit card transaction will be evaluated through IF and THEN tests. The transaction will
have to successfully satisfy these tests to be classified as a legit transaction. Credit card transactions
that fail to satisfy the mapped expected behavior are flagged as fraudulent and require the attention
of the card owner for further validation.
Hidden Markov Model
This type of credit card fraud detection technique utilizes the habitsof the credit card user to create
acceptable parameters for non- fraudulent transactions. A history ofcredit card transactions
combined with the cardholders new operations will help the system compute for probabilities. If the
probabilities are within the acceptable parameters set by the system, then the transaction is
considered legit, otherwise the attention of the card owner will be called for a suspicious credit card
operation.
These techniques are just some of the many credit card fraud detection methods that are employed
by various businesses and financial institutions. To date, credit card companies are continuously
developing better fraud detection methods to help them keep up with the new credit card fraud
threats in the industry.
