Dataset from Kaggle https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

• Data Collection: Start by collecting and cleaning historical data on fraudulent and non-fraudulent transactions from the financial services company. This data might include information such as transaction amount, time of day, location, and customer information.
• Feature Engineering: Next, you'll need to create meaningful features from the raw data that will be used to train the model. This could include aggregating transaction data at the customer level (e.g., total spend, average transaction size, etc.), calculating the difference between transaction time and customer time zone, and creating indicators for unusual transaction locations.
• Model Training: Using the pre-processed data, train a machine learning model such as a Random Forest, XGBoost or Neural Network to distinguish between fraudulent and non-fraudulent transactions. You'll want to split your data into training and testing sets, and use cross-validation techniques to evaluate the model's performance.
• Model Deployment: Once you have a model that performs well on the testing data, you can deploy the model in a real-time scoring system that can be integrated into the financial services company's existing systems. The model will then be used to predict the likelihood of fraud for each incoming transaction, and transactions with a high likelihood of fraud can be flagged for manual review.
• Model Monitoring and Maintenance: Finally, it's important to regularly monitor the model's performance and retrain it as needed. This could involve using techniques such as concept drift detection and incremental learning to detect when the underlying distribution of the data has changed, and updating the model accordingly.

1. Data Preparation: You can use the Pandas library to load and clean the transaction data. Pandas provides convenient data manipulation and analysis functions that will make it easier to pre-process the data.
2. Feature Engineering: To create meaningful features, you can use functions provided by Pandas and NumPy. For example, you can calculate aggregate statistics such as mean and standard deviation, create time-based features such as day of week, and create binary indicators for unusual transaction locations.
3. Model Training: To train the machine learning model, you can use the scikit-learn library, which provides a simple and efficient interface to a variety of machine learning algorithms, including Random Forest, XGBoost and Neural Networks. You can split the data into training and testing sets, and use cross-validation techniques to evaluate the model's performance.
4. Model Deployment: To deploy the model in a real-time scoring system, you can use the Flask web framework to create a RESTful API that exposes the model's predictions. You can then integrate this API into the financial services company's existing systems.
5. Model Monitoring: To monitor the model's performance, you can use the scikit-learn library's model evaluation functions, such as confusion matrix and ROC AUC, to calculate performance metrics. You can also use the joblib library to save and load the trained model, so that you can retrain it as needed.