AI in Finance Glossary

The simulation of human intelligence in machines that are programmed to think and learn like humans. In finance, AI is used for decision-making, pattern recognition, and automation.

A set of rules or instructions given to an AI system to help it learn and make decisions. In finance, algorithms are used for trading, risk assessment, and fraud detection.

A computing system inspired by biological neural networks. It consists of interconnected nodes that process information and can learn patterns from data.

A branch of AI that helps computers understand, interpret, and generate human language. Used in chatbots, sentiment analysis, and document processing.

A field of AI that enables computers to interpret and understand visual information from images or videos. Used in document processing, identity verification, and fraud detection.

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. Uses statistical techniques to give computers the ability to "learn."

A type of ML where the algorithm learns from labeled training data to make predictions on new, unseen data. Examples include credit scoring and fraud detection models.

A type of ML where the algorithm finds hidden patterns in data without labeled examples. Used for customer segmentation, anomaly detection, and market analysis.

A subset of ML that uses neural networks with multiple layers to model and understand complex patterns. Particularly effective for image recognition, natural language processing, and speech recognition.

A type of ML where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. Used in algorithmic trading and portfolio optimization.

The process of creating new features or modifying existing ones to improve ML model performance. Critical for financial modeling and risk assessment.

A modeling error that occurs when a model learns the training data too well, including noise and outliers, leading to poor performance on new data.

The use of computer algorithms to automatically execute trading strategies. AI-powered algorithms can analyze market data and execute trades at optimal times.

A digital platform that provides automated, algorithm-driven financial planning services with minimal human supervision. Uses AI to create and manage investment portfolios.

Technology that helps financial institutions comply with regulations efficiently and cost-effectively. Uses AI for monitoring, reporting, and risk management.

Technology innovations designed to improve the efficiency of the insurance industry. AI is used for risk assessment, claims processing, and customer service.

Banking services delivered through digital channels. AI enhances these services through personalized recommendations, fraud detection, and automated customer support.

A distributed ledger technology that maintains a continuously growing list of records. AI can be integrated with blockchain for smart contracts and automated compliance.

Extremely large datasets that can be analyzed to reveal patterns, trends, and associations. In finance, includes transaction data, market data, and customer behavior data.

The use of data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes. Used for credit scoring, fraud detection, and market forecasting.

The ability to process data as it arrives, enabling immediate analysis and response. Critical for fraud detection, algorithmic trading, and customer service.

The process of discovering patterns and relationships in large datasets. Used in finance for customer segmentation, risk assessment, and market analysis.

A set of rules that allows different software applications to communicate with each other. Essential for integrating AI services with existing financial systems.

AI systems that can explain their decision-making process in a way that humans can understand. Critical for regulatory compliance and building trust in financial AI applications.

The framework for managing AI/ML models throughout their lifecycle, including development, validation, deployment, and monitoring. Essential for regulatory compliance.

The process of identifying and mitigating unfair bias in AI models. Critical for ensuring fair lending practices and regulatory compliance.

European Union regulation that governs data protection and privacy. AI systems must comply with GDPR requirements for data processing and user consent.

The process of verifying that AI models perform as expected and meet regulatory requirements. Includes testing for accuracy, fairness, and robustness.