AI Glossary for Treasury
56 AI concepts explained in plain language, and a map of which AI technologies sit behind common treasury use cases. Keep it on your desk.
Core AI Concepts
- Artificial IntelligenceAI
- The field of computer science focused on creating systems capable of performing tasks that typically require human intelligence.
- Machine LearningML
- A subset of AI that enables computers to learn and improve from experience without being explicitly programmed for each task.
- Deep Learning
- A subset of ML that uses artificial neural networks with multiple layers to process complex data.
- Neural Networks
- Computational models inspired by the human brain, consisting of interconnected nodes that process information.
- Generative AI
- AI technology that can create new content (text, images, audio) based on training data.
- Large Language ModelsLLMs
- AI models trained on massive volumes of text to understand and generate natural language. Examples: GPT, Claude.
Types of Machine Learning
- Supervised Learning
- Learning with labeled data, where the model learns from examples with known answers.
- Unsupervised Learning
- Learning with unlabeled data, where the model discovers patterns and structures on its own.
- Reinforcement Learning
- Learning through trial and error, where the model receives rewards or penalties for its actions.
- Few-shot Learning
- Learning with very few training examples.
- Zero-shot Learning
- The ability to recognize categories never seen during training.
- Transfer Learning
- Using a pre-trained model for a new, related task.
Natural Language Processing
- NLPNatural Language Processing
- The field of AI dealing with interaction between computers and human language.
- NLUNatural Language Understanding
- The part of NLP focused on understanding the meaning of text.
- NLGNatural Language Generation
- The part of NLP focused on generating text in natural language.
- Tokenization
- The process of splitting text into smaller units (words, subwords) for model processing.
- Embeddings
- Numerical representations of words or phrases in multidimensional vector spaces.
- Sentiment Analysis
- Analyzing the sentiment or emotion expressed in text.
- Named Entity RecognitionNER
- Identifying entities in text such as people, places, organizations.
Key Architectures
- Transformers
- Neural network architecture that revolutionized NLP, based on the attention mechanism. Foundation of GPT, BERT, Claude.
- Attention Mechanism
- A technique allowing models to focus on relevant parts of input data.
- RNNsRecurrent Neural Networks
- Neural networks specialized for processing sequences of data.
- LSTMLong Short-Term Memory
- A special type of RNN capable of retaining information over long periods.
- Mixture of ExpertsMoE
- Architecture with specialized modules activated conditionally, enabling larger models with less compute.
- GANsGenerative Adversarial Networks
- Two neural networks competing to generate increasingly realistic content.
- Diffusion Models
- Generative models that create images by gradually removing noise from random data.
Technical Concepts
- Training Data
- The dataset used to train an AI model.
- Overfitting
- When a model learns the training data too well and fails to generalize to new data.
- Underfitting
- When a model is too simple to capture patterns in the data.
- Hyperparameters
- Configuration parameters of a model that are not learned automatically.
- Fine-tuning
- Adapting a pre-trained model to a specific task using additional training data.
- Prompt Engineering
- The practice of crafting effective inputs to get optimal responses from LLMs.
- RAGRetrieval-Augmented Generation
- Combining LLMs with external knowledge retrieval to improve accuracy and reduce hallucinations.
- Hallucination
- When an AI model generates plausible-sounding but factually incorrect information.
Applied AI & Automation
- RPARobotic Process Automation
- Automating business processes using software bots. Not AI, but often marketed as AI.
- OCROptical Character Recognition
- Recognizing and extracting text from images and scanned documents.
- Anomaly Detection
- Identifying unusual behaviors or data points that deviate from expected patterns.
- Time Series Forecasting
- Predicting future values based on historical time-ordered data.
- Chatbots
- Programs that simulate human conversation.
- Ensemble Methods
- Combining multiple models for improved performance. Includes Random Forest, Gradient Boosting.
Evaluation Metrics
- Accuracy
- The percentage of correct predictions out of total predictions.
- Precision
- The proportion of positive predictions that are actually correct.
- Recall
- The proportion of actual positive cases that are correctly identified.
- F1 Score
- The harmonic mean of precision and recall.
- Perplexity
- A measure of a language model’s uncertainty. Lower is better.
AI Infrastructure
- MLOps
- Practices for operationalizing machine learning models in production.
- Model Drift
- Degradation of model performance over time as real-world data changes.
- Data Pipeline
- An automated flow for processing and transforming data.
- A/B Testing
- Comparative testing of models under real conditions.
- Vector Database
- A database optimized for storing and querying embedding vectors, essential for RAG systems.
- Model Monitoring
- Continuous surveillance of model performance in production.
AI Safety & Ethics
- Explainable AIXAI
- Development of AI models whose decisions can be understood by humans.
- AI Ethics
- The study of moral and social implications of AI technologies.
- Federated Learning
- Training models on distributed data without centralizing it, preserving privacy.
- Adversarial Examples
- Data modified to fool AI models into making incorrect predictions.
- Privacy-Preserving AI
- Techniques for protecting data confidentiality while using AI.
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AI in Treasury: Technologies & Applications
Which AI techniques sit behind common treasury use cases, and examples of vendors and platforms operating in each area. Vendor names are examples, not recommendations; products and ownership change, so check each vendor directly.
Predictive Analytics & Forecasting
| Use case | Technologies | Example vendors |
|---|---|---|
| Cash Flow Forecasting | Time Series, LSTM, ARIMA, Prophet | Kyriba, Coupa Treasury (Bellin), SAP, Oracle |
| Liquidity Management | ML, Regression Models, Monte Carlo | ION Treasury (Reval), FIS Integrity |
| FX Rate Prediction | Deep Learning, RNNs, Transformers, Ensembles | Bloomberg Terminal, LSEG Workspace (Refinitiv Eikon) |
| Interest Rate Forecasting | Time Series, Bayesian Networks, Neural Networks | Moody’s Analytics, S&P Capital IQ |
Risk Management
| Use case | Technologies | Example vendors |
|---|---|---|
| Credit Risk Assessment | Supervised Learning, Random Forest, SVM | Moody’s RiskCalc, S&P CreditModel, FICO |
| Market Risk Analysis | Monte Carlo, VaR Models, ML | Murex, Calypso, FIS |
| Counterparty Risk | Classification, Ensemble Methods, Neural Networks | IBM OpenPages, Algorithmics |
| Operational Risk Detection | Anomaly Detection, Unsupervised Learning | SAS Risk Management |
Fraud Detection & Security
| Use case | Technologies | Example vendors |
|---|---|---|
| Transaction Fraud Detection | Real-time ML, Anomaly Detection, Graph Neural Networks | FICO Falcon, SAS Fraud Management |
| Payment Fraud Prevention | Deep Learning, Pattern Recognition | Featurespace ARIC, DataVisor |
| AML Compliance | NLP, Graph Analytics, Supervised Learning | NICE Actimize, Quantexa |
Process Automation
| Use case | Technologies | Example vendors |
|---|---|---|
| Invoice Processing & AP | OCR, NLP, Computer Vision, RPA | MindBridge, AppZen, Stampli |
| Bank Reconciliation | String Matching, Fuzzy Logic, ML | ReconArt, SmartStream, BlackLine |
| Regulatory Reporting | RPA, NLP, Data Mining | Wolters Kluwer, AxiomSL |
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