A subset of artificial intelligence that enables systems to learn from data, improving performance over time without explicit programming.

The process of using statistical techniques and algorithms to predict future outcomes based on historical data.

Innovative technology designed to improve and automate financial services, enhancing efficiency and accessibility.

The science of analyzing raw data to uncover patterns, trends, and insights that inform decision-making.

Information that is delivered immediately after collection, enabling timely decision-making and forecasting.

A statistical method used to evaluate the performance of a model by partitioning data into subsets for training and testing.

The process of connecting different software applications through application programming interfaces to enable data exchange.

Quantitative measures used to assess the effectiveness and accuracy of predictive models.

The process of correcting or removing inaccurate, incomplete, or irrelevant data from a dataset.

Step-by-step procedures or formulas for solving problems, particularly in data processing and machine learning.

The process of confirming that a predictive model performs accurately and reliably against new data.

The graphical representation of data and information to facilitate understanding and insights.

The practice of estimating future trends based on historical data and analysis.

Specific algorithms used in machine learning to analyze data and make predictions, such as regression and classification.

A continuous process of refining models based on performance feedback and new data.

Comparing a model's performance against industry standards or best practices to assess its effectiveness.

The process of tracking and managing the movement of data within an organization or system.

Deep understanding derived from data analysis that informs strategic decisions.

Common obstacles faced when implementing machine learning, such as data quality and algorithm selection.

Emerging innovations and technologies that are shaping the future of financial services and operations.

Involving relevant parties in discussions to ensure their insights and needs are considered in decision-making.

An engaging story or explanation that effectively communicates complex ideas to an audience.

A practice of writing reflections on learning experiences to enhance understanding and self-assessment.

Constructive criticism and suggestions provided by colleagues to improve work and learning outcomes.

The use of mathematical and statistical methods to evaluate data and derive insights.