The process of identifying and preventing fraudulent activities using various techniques, including machine learning.

A subset of AI that enables systems to learn from data and improve their performance over time without explicit programming.

The process of selecting, modifying, or creating features from raw data to improve model performance.

Financial systems that facilitate banking operations, including transactions, account management, and fraud detection.

The capability to analyze data as it is created, allowing for immediate detection and response to fraud.

Assessing the performance of a machine learning model using metrics like accuracy, precision, and recall.

The process of optimizing model parameters to enhance its performance during training.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

A table used to evaluate the performance of a classification model, showing true vs. predicted classifications.

A graphical representation of a model's true positive rate against its false positive rate at various threshold settings.

Choosing the most appropriate machine learning algorithm based on the data and problem type.

The steps taken to clean and prepare raw data for analysis, enhancing its quality and usability.

The continuous observation of transactions to detect suspicious activities in real-time.

Identifying patterns in data that do not conform to expected behavior, often used in fraud detection.

The ratio of true positive predictions to the total predicted positives, indicating the accuracy of positive predictions.

The ratio of true positive predictions to the actual positives, measuring the model's ability to find all relevant cases.

A harmonic mean of precision and recall, providing a single metric to evaluate model performance.

The plan for integrating a machine learning model into a production environment for real-world use.

A method of continuously ingesting and analyzing data in real-time, essential for fraud detection systems.

The condition of a dataset, determined by accuracy, completeness, consistency, and reliability.

Methods used to assess the reliability and performance of a model before deployment.

A system designed to handle increasing amounts of work or data efficiently, crucial for growing banking operations.

The examination of financial transactions to identify patterns, anomalies, or fraudulent behavior.

The process of effectively conveying findings and insights to relevant parties in the banking sector.

Adhering to laws, regulations, and guidelines to ensure that banking practices meet legal standards.