Credit Card Fraud Detection Using Machine Learning

Credit Card Fraud Detection with Machine Learning: Development and Implementation

Based on the table above we can say that machine learning for finance is an advanced way for card fraud monitoring, preferred by businesses endeavoring to elevate software security. Businesses often develop ML models specifically for credit card fraud detection, training these models on historical transaction data to identify and prevent fraud schemes.

According to the Statista report, nearly 40% of companies used automation systems, including ML-based solutions, for fraud detection in 2023. But what are the steps involved in preventing fraud using ML? The procedure starts with the following seven stages.

Stage 1: Objectives and KPIs Clarification

It will be a good idea to start through aligning your ML initiative consistent with organizational targets and objectives. Every business that wants to implement fraud detection strives to reduce fraud-related losses while making certain that security measures will not negatively affect customer experience. 

So, before moving to the rollout steps, it is important to set specific KPIs for your solution, including accuracy, precision, recall, and false-positive rates. Additionally, make an effort to strike a perfect balance between detecting fraud and not slowing down the speed of transactions for end users. Work with your team on how your future solution will achieve that on a strategic level. 

Stage 2: Data Collection 

In the context of preventing credit card scams, it comprises collecting data from different sources such as transaction logs, customer information databases, device details, external data sources, and historical fraud data. Gathering diverse and thorough training data from multiple sources is vital to improve the model’s ability to detect various fraud scenarios and increase overall system strength. Data preparation usually entails loading the dataset into a DataFrame and reviewing its structure to understand the features available for modeling. Additionally, data streaming technologies like Apache Kafka and Apache Flink are standard tools for live data ingestion and processing, supporting immediate detection of fraud schemes.

However, the raw data obtained from these sources often contains inconsistencies, missing values, outliers, and other issues that need to be addressed before it can be effectively used to spot frauds.

Stage 3: Data Preprocessing

To clean, transform, and prepare the raw data for analysis, ML engineers apply data preprocessing techniques. This involves several steps:

• Examination: Before preprocessing, it is important to check the shape of the dataset to understand its dimensions (number of rows and columns). Using print statements in code to output the shape and other characteristics of the data during preprocessing helps in debugging and verifying data integrity.
• Data Cleaning: Involves handling missing values, correcting inconsistencies, and removing duplicates. For example, missing values in transaction amounts or timestamps might be imputed using statistical methods or filled with appropriate defaults.
• Outlier Detection and Handling: Outliers can significantly affect the performance of ML models. Techniques such as Z-score, interquartile range (IQR), or clustering-based approaches can be used to detect and handle outliers.
• Feature Scaling: Features in the dataset may have different scales, which can lead to biased models. Techniques such as normalization or standardization are applied to bring all features to a similar scale.
• Encoding Categorical Variables: Categorical variables such as merchant IDs or transaction types need to be encoded into numerical representations before being fed into ML algorithms. Techniques like one-hot encoding or label encoding are commonly used for this purpose.
• Feature Engineering: Feature engineering included creating unique identifiers for merchants and customers, as well as temporal features like ‘is_weekend’ and ‘is_night’ to capture transaction patterns.
• Data Transformation: Transformation techniques like logarithmic transformation or Box-Cox transformation may be applied to make the data more symmetrical and improve model performance.

Stage 4: Feature Engineering

Next comes feature engineering crucial for enhancing the predictive power of ML models in fraud monitoring. It involves creating new features or variables that capture relevant information about fraudulent activities. Feature engineering techniques extract, transform, and aggregate information from the preprocessed data to create new features. Features are often transformed for privacy or modeling purposes, such as converting identifiers or applying dimensionality reduction. For example, PCA transformation can be applied to numeric variables to protect customer privacy while retaining essential patterns for analysis in detecting fraudulent transactions. Some common feature engineering techniques in monitoring frauds with financial card include:

• Time-Based Features: Features derived from transaction timestamps such as time of day, day of week, or time since last transaction. Fraudulent activities may exhibit specific patterns at short periods of time.
• Transaction Amount Statistics: Aggregations and statistics derived from transaction amounts, such as mean, median, standard deviation, maximum, minimum, and percentiles. Here, anomaly detection with machine learning helps to spot fraudulent behavior.
• Merchant and Customer Behavior Features: Features capturing patterns in merchant-customer interactions, such as frequency of credit card transactions with specific merchants, average transaction amounts per merchant, or unusual changes in spending behavior.
• Device and Location Features: Features related to the device used for transactions (e.g., device ID, IP address) and transaction locations (e.g., geolocation, distance between transaction locations). Sudden changes in device or location may indicate suspicious activity.
• Aggregated Features: Features derived from aggregating historical transaction data, such as the number of previous payments within a certain timeframe, or the frequency of transactions from high-risk locations.

Stage 5: Model Training and Evaluation

After feature engineering, ML models are trained on the preprocessed and feature-engineered data to learn patterns and generate predictions. The dataset is typically split into training and test data, with the test data used to evaluate how well the model applies to unseen transactions. Different ML algorithms such as logistic regression, random forests, gradient boosting, or deep learning models like neural networks are trained on the datasets.

These models are subsequently used to predict whether a given transaction is fraudulent or legitimate based on transaction data. The trained models are evaluated using evaluation criteria such as, precision, recall, F1-score, and ROC-AUC on a separate test dataset to analyze their effectiveness in detecting fraudulent transactions. A confusion matrix is often used to visualize the number of true positives, false positives, true negatives, and false negatives, giving perspective on model strengths and weaknesses. It is important to note that imbalanced datasets can lead to misleading model effectiveness metrics, as high accuracy can be achieved by simply predicting the majority class. Techniques like cross-validation and hyperparameter tuning are implemented to boost model effectiveness and avoid overfitting.

Stage 6: Deployment and Setting Up Feedback Loop

Once evaluated, the best-performing model is deployed into production where it continuously monitors incoming payments, flagging potentially suspicious transactions for further review or action.

At this stage, one of the most important aspects is to establish an effective feedback loop and improve the performance of the model over time. Encouraging user engagement and collecting feedback from users or analysts is fundamental, as their engagement supports pinpoint opportunities for enhancement and refines the fraud detection system. This process includes regular data collection on flagged transactions, and manually reviewing outcomes of fraud investigations. The data from the reviews must be fed into the model, so the credit card fraud detection system using ML will be able to learn from the mistakes and reduce false positives.

Stage 7: Continuous Improvement

Right after the deployment, the process of continuous improvements begins, as there is no limit to perfection. Fraud patterns evolve constantly, so ensure that your model is kept updated with new information and is future-proofed to face upcoming threats. It is also very important to monitor data and model effectiveness for concept drift, where the statistical properties of the target variable change over time, decreasing model effectiveness.