Credit card fraud is a growing problem that affects millions of people worldwide and costs businesses billions of dollars every year. As fraudsters become more sophisticated, traditional fraud detection methods are slow to respond. As a result, the only solution left for businesses is to invest in the latest technology.
One of the things technology experts are incorporating into their businesses is machine learning (ML), a subset of artificial intelligence (AI). These models have shown great potential in detecting and preventing fraud, providing a financially more secure future for everyone.
Common credit card fraud methods
Credit cards are the most frequently reported method of payment fraud. According to the FTC, credit cards top the list of types of identity theft, with the FTC receiving 416,582 reports in 2023. The median loss per fraud report was $500.
Some of the most common ways to commit credit card fraud are the following tactics:
- Card Skimming: Fraudsters use devices that capture credit card information from magnetic strips during legitimate transactions.
- Phishing: Scammers trick individuals into providing their card details through fake emails, messages or websites.
- Card Not Present (CNP): Fraudsters use stolen credit card information to make purchases online or over the phone where no physical card is needed.
- Account Takeover: Criminals often gain access to individuals' account details through data breaches or social engineering and then use the accounts to make fraudulent transactions.
- Application fraud: People use stolen or fake identities to apply for new credit cards to make purchases. Loan fraud is the third most common form of identity theft today.
- Synthetic identity fraud: Scammers mix real and fake information to create a new fake identity and apply for a loan.
There are many ways credit card fraud can be committed, and machine learning models are key to quickly and accurately detecting and preventing it.
How machine learning helps detect credit card fraud
Machine learning can detect credit card fraud by analyzing transactional datasets to identify patterns and anomalies that indicate fraudulent activity. Machine learning differs from traditional rule-based systems that flag suspicious activity based on predefined criteria.
Instead, they continuously learn and adapt to new data, which allows the models to detect the most subtle fraud patterns, even as they evolve. Plus, studies have shown that ML can identify up to 100% of fraudulent transactions in real time.
These models evolve through supervised and unsupervised learning techniques. In supervised learning, data engineers train models on datasets containing known fraud cases, an activity that allows them to recognize similar patterns in new transactions.
Unsupervised learning, on the other hand, detects anomalies in transaction data without any prior knowledge of what constitutes fraud. Both methods can be used to train machine learning systems to recognize known fraudulent patterns while also identifying new and emerging threats.
Implementing Machine Learning to Counter Credit Card Fraud
Data scientists use billions of data points in ML training to detect fraud, but implementing ML requires several steps.
1. Data Collection
The first step involves collecting large amounts of transaction data from various sources, including credit card companies, banks, and financial institutions. This data typically includes details such as transaction amount, timestamp, location, merchant information, and cardholder details.
Additionally, the collected data must be labeled with which transactions are legitimate and which are fraudulent. This provides ML with a basis to train models to learn patterns and characteristics of normal and fraudulent transactions. However, raw data may contain inconsistencies and outliers that require further action by data engineers.
2. Data Preprocessing
Once data engineers have collected the data, the next step is to clean and prepare the data to make it suitable for analysis. Data preprocessing involves several tasks, such as removing duplicates and normalizing the information to put all variables on a similar scale. It also involves converting categorical data into a numerical form that ML algorithms can interpret. Effective data preprocessing ensures that the dataset is accurate and ready for analysis by ML models.
3. Feature Engineering
Feature engineering is the process of selecting, modifying, or creating new features from raw data. This step helps improve the performance of ML models in detecting credit card fraud. It is very important to perform this step because the quality and relevance of the features used in the model significantly impacts the accuracy and effectiveness of the model.
Fraud detection takes into account features such as time of day, merchant category, geographic location, transaction frequency, transaction amount, etc. Additionally, data scientists can gain deeper insights from ML models by creating derived features such as average transaction amount over a period of time or deviations from typical spending patterns.
Overall, feature engineering is key to improving machine learning and its predictive capabilities.
4. Training and evaluating the model
After feature engineering, data scientists need to train the ML model with the prepared dataset. During training, the model learns to recognize patterns and correlations between features and labels (fraudulent or legitimate transactions). Mathematicians also use different algorithms depending on the complexity and requirements of the detection system.
Once the model is ready, ML engineers need to evaluate its performance using a separate validation dataset. Key evaluation metrics include accuracy, precision, recall, and F1 score, each of which helps determine how accurately the model can identify transactions while minimizing false positives and false negatives.
Data scientists also use cross-validation techniques to verify the robustness and generalizability of the models. Once evaluated, the models are continuously monitored and retrained on new data to maintain their validity.
Using machine learning to prevent fraud
Machine learning is revolutionizing the financial industry by providing greater accuracy, real-time detection, and adaptability to new fraud patterns. Financial institutions and credit card companies can significantly improve fraud detection rates by investing in these advanced algorithms. Machine learning technology is essential to stay ahead of the latest fraud techniques and ensure consumers' financial security.
