A branch of advanced analytics that uses historical data to forecast future outcomes, particularly in financial contexts.

A subset of artificial intelligence focused on building systems that learn from data, improving their performance over time without being explicitly programmed.

The process of evaluating the likelihood that a borrower will default on a loan, utilizing various quantitative and qualitative factors.

The process of cleaning and organizing raw data into a usable format, essential for effective analysis and model building.

The principles guiding the responsible use of AI and machine learning, ensuring fairness, transparency, and accountability in decision-making.

A statistical method for predicting binary outcomes, commonly used in credit risk modeling to classify borrowers as low or high risk.

A flowchart-like model used for classification and regression tasks, providing clear decision paths based on feature values.

The process of assessing a predictive model's performance using various metrics to ensure reliability and accuracy.

A modeling error that occurs when a model learns noise in the training data, leading to poor performance on unseen data.

A scenario where a model is too simple to capture the underlying trend of the data, resulting in poor predictive performance.

The process of selecting, modifying, or creating variables (features) to improve the performance of machine learning models.

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

A graphical representation of a model's diagnostic ability, plotting true positive rates against false positive rates.

A table used to evaluate the performance of a classification model by summarizing true positives, false positives, true negatives, and false negatives.

The process of optimizing the parameters that govern the learning process of a machine learning model to improve performance.

The accuracy and consistency of data over its lifecycle, crucial for reliable analysis and model development.

Standards set by governing bodies to ensure compliance and ethical practices in the use of AI and machine learning in finance.

Systematic errors in model predictions due to prejudiced training data or flawed algorithm design, impacting fairness.

Methods used to represent data graphically, aiding in understanding patterns and insights from the data.

A mathematical model that uses historical data to predict future outcomes, vital in credit risk assessment.

The practical implementation of theoretical concepts in actual financial scenarios to assess their effectiveness.

A pedagogical approach that encourages deep understanding and application of knowledge, fostering critical thinking.

An interdisciplinary field that uses scientific methods, processes, and algorithms to extract insights from structured and unstructured data.

Techniques and practices used to identify, assess, and mitigate risks in financial decision-making.

The extent to which the workings of an AI system are understandable and accessible to users and stakeholders.