Credit Card Fraud Detection prototype in Banking & Finance theme harnessing various Google Cloud’s services along with Source Code
Introduction :
From traditional to emerging sectors, there is not one single business that is fully immune from fraud. Fraud is a universal threat, impacting businesses of all sizes and sectors. Studies suggest various types of fraud cost companies a staggering 1%-1.75% of their annual sales, translating to a whopping $200 billion globally every year.
One of the most common and costly forms of fraud is credit card transaction fraud. In the United States alone, an estimated 127 million people have been affected, with attempted fraudulent charges on credit and debit cards reaching approximately $8 billion.
This alarming reality highlights the critical need for credit card companies to develop robust fraud detection strategies. By understanding the characteristics of fraudulent transactions, they can build sophisticated predictive models that flag suspicious activity. These models play a vital role in preventing fraud and protecting both consumers and financial institutions.
Credit Card Fraud Analysis and Modeling
Credit card fraud is a major concern for both financial institutions and consumers. Fortunately, data analysis and modeling techniques can help detect fraudulent transactions before they occur. This section will specifically focus on using K-Nearest Neighbors (KNN) for credit card fraud analysis and modeling.
Why KNN for Credit Card Fraud Detection?
KNN is a popular choice for credit card fraud detection due to several reasons:
- Simplicity: KNN is a relatively easy algorithm to understand and implement, making it accessible even for those without a strong machine learning background.
- Interpretability: KNN provides insights into why a transaction is classified as fraudulent. By analyzing the nearest neighbors of a flagged transaction, we can understand the characteristics that triggered the alert.
- Effectiveness: KNN can be effective in detecting various types of fraud, especially when dealing with imbalanced datasets, which are common in fraud detection (fraudulent transactions are a small portion of all transactions).
How KNN Works for Fraud Detection:
- Data Preparation: Transaction data is collected and preprocessed. This includes cleaning the data, handling missing values, and potentially scaling features to ensure all have a similar impact on the model.
- Feature Selection: Relevant features for identifying fraud are chosen. These features might include transaction amount, location, time, cardholder information, purchase history, etc.
- Model Training: Historical transaction data, labeled as fraudulent or legitimate, is used to train the KNN model.
- Fraud Prediction: For a new transaction, the model identifies its k-nearest neighbors (k most similar transactions) based on the chosen features. The majority class (fraudulent or legitimate) among the k-nearest neighbors determines the classification of the new transaction.
Challenges and Considerations:
- Choosing the optimal k value: The number of neighbors (k) significantly impacts the model’s performance. Experimentation is needed to find the best k value for your specific dataset.
- Imbalanced Datasets: As mentioned earlier, KNN can handle imbalanced datasets well. However, it’s important to monitor the model’s performance on both fraudulent and legitimate transactions.
- Feature Selection: Choosing the most relevant features is crucial for KNN’s effectiveness. Feature engineering techniques can help improve the model’s accuracy.
Source Code can be obtained in the Below https://github.com/drraghavendra/Credit-Card-Fraud-Detection-Using-Google-Cloud-Services
Conclusion
Google cloud services andKNN is a valuable tool for credit card fraud detection. Its simplicity, interpretability, and effectiveness with imbalanced datasets make it a popular choice. However, careful consideration of factors like k value selection and feature engineering is necessary for optimal performance.
