In the era of AI, you don’t need to be a data scientist to lead a banking team, but you do need to speak the language. Walking into a boardroom and calling every automated process “the algorithm” is a quick way to lose credibility with the innovation departments.
Whether you are in M&A, Wealth Management, or Compliance, these 10 terms are the new alphabet of modern finance. Here is your Algoy Cheat Sheet to keep you ahead of the curve in the age of autonomous systems.
1. Agentic AI (The “Do-Bots”)
The Definition: Unlike a standard chatbot that just provides text, an Agent can actually act. It can use tools, access your bank’s API, and complete multi-step tasks without constant human prompting.
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Meeting Context: “We aren’t just building a help desk; we’re deploying Agentic AI to automate the entire mortgage subordination process.”
2. RAG (Retrieval-Augmented Generation)
The Definition: RAG connects a Large Language Model to your bank’s private, secure data. It prevents the AI from “guessing” by forcing it to look at your specific PDFs and databases before generating an answer.
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Meeting Context: “To ensure 100% accuracy in our internal policy searches, we are implementing a RAG pipeline rather than relying on a general model.”
3. Hallucination
The Definition: When an AI confidently states a fact that is completely false—such as making up a fake interest rate or a non-existent regulation.
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Meeting Context: “We need a secondary ‘Auditor Agent’ to minimize hallucination risk in our automated credit memos.”
4. Vector Database
The Definition: A specialized storage system that allows AI to find “related” concepts mathematically. It is the “brain” that allows RAG to find the right information instantly.
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Meeting Context: “Is our Vector Database hosted on-premise to comply with local data sovereignty and privacy laws?”
5. Tokenization
The Definition: How an AI “reads.” Instead of words, it breaks text into “tokens” (chunks of characters). In the era of AI, “Token Limits” have replaced “Page Limits” as the primary constraint for data processing.
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Meeting Context: “We need to optimize the token usage for our high-volume KYC scanning to keep API costs under budget.”
6. Fine-Tuning
The Definition: Taking a general AI and training it further on a specific dataset—like ten years of your bank’s specific loan outcomes—to make it an expert in your business logic.
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Meeting Context: “Instead of a generic model, we are fine-tuning an open-source model on our proprietary wealth management data.”
7. Zero-Shot Prompting
The Definition: Asking an AI to do a task it has never specifically been trained for, with no prior examples given in the chat.
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Meeting Context: “The model’s zero-shot performance on complex derivative legal text is surprisingly high.”
8. PII Redaction (Personally Identifiable Information)
The Definition: The process of automatically “blacking out” names, social security numbers, and addresses before data is processed by an AI to ensure client confidentiality.
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Meeting Context: “Our pipeline includes an automated PII Redaction layer to ensure we stay compliant with global data protection standards.”
9. RLHF (Reinforcement Learning from Human Feedback)
The Definition: The process where human experts “coach” an AI by grading its answers. It is the critical “polishing” phase of an AI model’s development.
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Meeting Context: “We need our senior analysts to participate in the RLHF phase to ensure the AI’s tone matches our institutional brand voice.”
10. Compute (The “Fuel”)
The Definition: The raw processing power (GPUs) required to run AI models. In the modern economy, “Compute” is as strategic a resource as capital.
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Meeting Context: “Do we have the compute capacity to run this model locally, or do we need a cloud-bursting agreement for peak times?”
📊 The Banker’s Vocabulary Matrix
| If you want to say… | Use this term instead… | Why it sounds better |
| “The AI is lying.” | Hallucination | It is the industry-standard technical term for errors. |
| “The AI is doing tasks.” | Agentic Workflow | It implies autonomy and sophisticated process. |
| “AI using our data.” | RAG Implementation | It specifies the method of secure data usage. |
| “Training the AI.” | Fine-Tuning | Training is vague; fine-tuning is the specific process. |
💡 The Algoy Takeaway
In the boardroom of the AI era, the person who understands the mechanics of the system (RAG, Tokens, Compute) will always hold more influence than the person who only understands the magic. Use these terms to demonstrate that you don’t just use the tools—you understand the engine behind them.
