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Victor Isaac Oshimua
Victor Isaac Oshimua

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Credit Card Fraud Detection: A Machine Learning Approach To Combat Financial Fraud

Anyone can be a victim of financial fraud if you're buying or selling things online, processing online payments, providing financial services, etc. There are chances you might get defrauded. Victims of this fraud are not limited to individuals alone. Businesses get victimised by crooks as well; scams like this are even more deadly for businesses because, apart from losing tonnes of money, they also lose customers; therefore, detecting and preventing fraud is essential.

Over the past two decades, the financial losses resulting from credit card theft have surged to reach a staggering $14.23 billion. Specifically, $5.8 billion was attributed to fraudulent activities, marking a significant increase of $2.4 billion compared to the previous year. In 2021, the global losses due to credit card theft surpassed a staggering $32 billion, with the United States alone accounting for a substantial loss of $12 billion. - Earl Rinand Margallo

This leads us to the question, How can credit card fraud be detected and prevented?

How does credit card fraud work?

Credit card fraud is simply any fraud or theft that involves a credit card.
Common forms of credit card fraud include:

  • Usage of Lost or stolen cards: This happens when stolen or lost credit cards are used without the owner's authorisation.

  • Credit card skimming: This is a situation where the card is cloned or copied using a special swipe machine to create a duplicate of the credit card.

  • Card-not-present fraud: Credit Card details, such as the card number, cardholder name, date of birth, and address, are stolen from online databases or through email scams. These stolen details are then sold and utilised for fraudulent activities.

  • Identity theft: Fraudulent applications are made in someone else's name for a new credit card without the person's knowledge or consent.

Approaches to Detect and Prevent Credit Card Fraud

There are two approaches to combating credit card fraud: The common one is using a rule-based system, and the effective one is the application of machine learning (ML).
This article focuses on the ML approach. Nevertheless, let's start with the common approach.

Rule-based system

A rule-based system uses predefined rules to make decisions. It matches conditions in the rules to available data and executes actions. The rules are created by human experts with domain knowledge.
These rules are written as IF-THEN-ELSE statements using a computer programming language. To show how a rule-based system works, Let's look at how rule-based systems are used to detect fraud.

Behind any fraud, there is a pattern that becomes visible when experts analyse the activity.

Rule based system with expert domain knowledge

This pattern is used to create thousands of rules, which are used to detect fraud in new transactions.
For example, these rules can be:

  • If the transaction is over a limit, then "fraud"
  • If the transaction is from an unusual location, then "fraud"
  • If the transaction IP address is blocked, then "fraud"

Rule based system

These rules are updated regularly because fraudsters are smart too; they try to carry out fraudulent activities that can beat these rules. For instance, if fraudsters notice this rule system doesn't allow transactions from a certain location, they might use a VPN to hide their locations.

These rules are manually written by experts, making it difficult to update them when new fraud patterns are found. As a result, certain fraudulent activities can go unnoticed. However, there is an effective solution to this problem: Machine learning.

Machine learning system

Machine learning is a branch of artificial intelligence that teaches machines to predict future occurrences using past data without being explicitly programmed. Machine learning systems excel at identifying patterns, often surpassing human capabilities.
So, how exactly do machine learning systems detect fraud?

Historical data: Historical data: A machine learning system needs to learn from historical data, which includes various features and details of past transactions such as the time of the transaction, amount of the transaction, distance from the last transaction, and more. Additionally, the historical data includes a target label indicating whether each transaction is fraudulent or not.
Some of these features may be irrelevant for detecting fraud. Therefore, a data scientist analyses these features and identifies the ones with the most predictive power. After all the important features are selected, a machine learning model is trained using machine learning algorithms, and this model can be used to predict fraud in new transactions.

Machine learning method

Anomaly detection: E-commerce merchants are frequently targeted by financial fraud due to the nature of online account access and customer transactions. An e-commerce company can employ anomaly detection techniques to identify potential fraudulent activity within its customer base. This is done by utilising behavioural analytics to segment customers into different groups based on their purchasing behaviour, transaction history, browsing patterns, and other relevant factors.

Once the customer segments are established, the company applies anomaly detection algorithms to compare the behaviour of individual customers to that of their respective groups. The algorithms analyse various features such as transaction frequency, average order value, time of purchase, geographic location, and more.

For instance, a specific customer falls within a group of frequent shoppers with consistent purchasing patterns. If this customer suddenly exhibits unusual behaviour, such as a significant increase in transaction volume, it may raise an anomaly alert, and the transaction will get flagged.

Deep neural network: Machine learning systems are prone to errors, and many fraud detection systems based on machine learning are not 100% effective. These systems can make mistakes, resulting in false positives and false negatives. A false positive occurs when the system incorrectly detects fraud where there is none, while a false negative happens when there is fraud but the system fails to detect it.

In reality, non-fraudulent transactions outnumber fraudulent ones, creating a class imbalance. This class imbalance leads to the occurrence of false positives and false negatives. Flagging a transaction as fraudulent when it is not can cause a business to lose a customer. To address this challenge, machine learning employs a method called deep neural networks.

Deep neural networks are a subset of machine learning that uses mechanisms inspired by the human brain. These mechanisms allow the networks to find patterns in massive amounts of data without manually selecting features. This complex processing enables neural networks to learn from vast amounts of data and identify more fraud patterns, making the fraud detection system almost error-proof.

How does fraud still happen?

Despite the application of machine learning to detect fraud, individuals and organisations are still not completely safe from financial fraud. It is important to note that technological advancements don't solely benefit law-abiding individuals.
Unfortunately, fraudsters are smart and exploit the power of modern technology to carry out scams using increasingly sophisticated methods.
However, businesses possess a powerful advantage. Many businesses collect and store large amounts of transaction data, yet they have not fully learned to leverage it through comprehensive analysis. The challenge lies in fully utilising this data to its maximum potential, a potential that only machine learning can uncover.


Machine learning is a powerful tool in the fight against financial fraud. Businesses and organisations can enhance their fraud detection capabilities by analysing historical data, utilising behavioural analytics, and employing anomaly detection techniques. However, it's important to acknowledge that fraudsters are continuously adapting and leveraging advancements in technology for their malicious purposes. This highlights the ongoing need for vigilance, continuous improvement, and the adoption of robust fraud prevention measures. By staying proactive and leveraging the potential of machine learning, we can strive to stay one step ahead in the battle against financial fraud and safeguard the interests of individuals and businesses alike.

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