Credit card fraud is a growing problem that affects millions worldwide and costs businesses billions of dollars every year. As fraudsters become more sophisticated, traditional methods of detecting fraud become too slow. As a result, companies have one solution left — investing in the latest technology.
One piece of technology professionals are integrating into their businesses is machine learning (ML), a subset of artificial intelligence (AI). These models are showing immense potential in detecting and preventing fraudulent activity, providing everyone with a more financially secure future.
Common credit card fraud tactics
Credit cards are the most frequently reported method of payment fraud. According to the FTC, credit cards sit at the top of the list of identity theft types, with the FTC receiving 416,582 reports in 2023. The median loss for each fraud report was $500.
Some of the most common ways people commit credit card fraud is through the following tactics:
- Card skimming: Fraudsters use devices to capture credit card information from the magnetic strip during legitimate transactions.
- Phishing: Scammers trick individuals into providing their card details through deceptive emails, messages or websites.
- Card Not Present (CNP): Fraudsters will use stolen credit card information to make online or over-the-phone purchases where a physical card is not required.
- Account takeover: Criminals gain access to a person’s account details — often through data breaches or social engineering — and use the account for 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 to apply for credit.
With various methods of credit card fraud, machine learning models are the key to quickly and accurately detecting and preventing it.
How machine learning helps with detecting credit card fraud
Machine learning can detect credit card fraud by analyzing transaction data sets to identify patterns and anomalies that indicate fraudulent activity. ML is unlike traditional rule-based systems, where they rely on predefined criteria to flag suspicious activity.
Instead, it continuously learns and adapts to new data. This allows the models to detect even the most subtle fraud patterns, even when evolving. Additionally, research shows ML can identify up to 100% of fraudulent transactions in real time.
These models advance through supervised and unsupervised learning techniques. In supervised learning, data engineers train the model on a dataset that includes known instances of fraud. This activity enables them to recognize similar patterns in new transactions.
On the other hand, unsupervised learning involves detecting anomalies in transaction data without prior knowledge of what constitutes fraud. Utilizing both methods to train machine learning systems allows them to recognize known fraudulent patterns while identifying new and emerging threats.
Implementing machine learning for credit card fraud
Data scientists use billions of data points in ML training to detect fraud. However, implementing ML requires a few steps.
1. Data collection
The first step involves gathering large amounts of transaction data from various sources, including credit card companies, banks and financial institutions. This data typically includes details such as transaction amounts, time stamps, locations, merchant information and cardholder details.
Additionally, the collected data should label which transactions were legitimate and which were fraudulent. This gives the ML a foundation for training the models, enabling them to learn the patterns and characteristics of normal and fraudulent transactions. However, raw data may contain inconsistencies and outliers that require data engineers to address further.
2. Data preprocessing
Once the data engineer collects the data, the next step is to clean and prepare it to ensure it is suitable for analysis. Data preprocessing includes several tasks, such as removing duplicates and normalizing the information to bring all variables to a similar scale. It also involves turning categorical data into numerical formats for ML algorithms to interpret. Effective data preprocessing ensures the dataset is accurate and ready for the ML model to analyze.
3. Feature engineering
Feature engineering is the process of selecting, modifying and creating new features from the raw data. This step helps to improve the performance of ML models in detecting credit card fraud. It is critical to take this step because the quality and relevance of the features used in the model greatly impact its accuracy and effectiveness.
In fraud detection, features include the time of day, merchant category, geographical location, frequency of transactions and transaction amount. Additionally, data scientists can gain deeper insights from ML models by creating derived features such as average transaction amount over a period or deviations from typical spending patterns.
Overall, feature engineering is key to improving machine learning and its predictive capabilities.
4. Model training and evaluation
After feature engineering, data scientists must use the prepared dataset to train ML models. During training, the model learns to recognize patterns and correlations between the features and labels — fraudulent or legitimate transactions. Mathematicians will also employ various algorithms depending on the complexity and requirements of the detection system.
Once the model is ready, ML engineers must evaluate its performance using a separate validation dataset. Key evaluation metrics include accuracy, precision, recall, and the F1 score. Each of these helps determine how well the model can identify model transactions while minimizing false positives and negatives.
Data scientists will also use cross-validation techniques to validate the model’s robustness and generalizability. Once evaluated, they will continuously monitor and retrain with new data to maintain the model’s effectiveness.
Leveraging machine learning for fraud prevention
Machine learning is revolutionizing the finance industry by offering higher accuracy, real-time detection and adaptability to new fraud patterns. When institutions and credit card companies invest in these advanced algorithms, they can boost fraud detection rates significantly. Machine learning technology is crucial for staying ahead of the latest fraud tactics and ensuring financial security for consumers.
