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AI GLOSSARY
AI TERMS FOR COMPLIANCE PROFESSIONALS

Red Oak

OVERVIEW

  • SUMMARY

The provided text offers an AI glossary specifically curated for compliance professionals, highlighting the transformative potential of artificial intelligence in the financial services sector. It illustrates a progression from foundational rule-based systems to sophisticated generative AI and large language models (LLMs), emphasizing their application in enhancing efficiency and security within compliance processes. The glossary defines various AI terms, ranging from concepts like Black Box Models and Explainable AI (XAI) to specific technologies such as ChatGPT, Claude, and Gemini, explaining their relevance and utility in regulatory contexts. Furthermore, it introduces practical approaches like Bring Your Own Model (BYOM) and crucial considerations such as guardrails and managing hallucinations to ensure ethical and effective AI integration in compliance.

CRITICAL QUESTIONS POWERED BY RED OAK

In regulated industries, every compliance decision must be defensible. Black box AI models may deliver accurate outputs, but without transparency, they create regulatory risk. Explainable AI ensures that compliance teams—and regulators—can understand why an AI produced a result. This builds trust, supports audits, and allows firms to demonstrate accountability while still leveraging advanced AI tools.

BYOM allows firms to choose the AI model that best fits their risk tolerance, security standards, and business goals. Whether it’s Claude, ChatGPT, or Gemini, organizations can integrate their preferred LLM directly into Red Oak’s AI Review module. This flexibility avoids vendor lock-in, supports evolving firm policies, and ensures compliance leaders remain in control of how AI is applied.

Speaker 1
Right. It's a hot topic.
Speaker 2
Definitely. We're using an AI glossary for compliance professionals as our guide here. Trying to cut through some of the jargon, you know.
Speaker 1
Yeah, demystify it a bit.
Speaker 2
Exactly. Our mission is really to equip you with what you need to understand what's happening now and well, what's coming down the pike.
Speaker 1
And it's fascinating because AI isn't just theory anymore, is it? It's actively transforming sectors like finance.
Speaker 2
Totally.
Speaker 1
So this dive, it's not just definitions. It's about why these terms actually matter for anyone dealing with modern compliance, like on a practical level.
Speaker 2
So the source maps out this journey. It talks about starting with lowercase AI.
Speaker 1
Hmm, lowercase AI. I like that framing.
Speaker 2
Yeah. Think back to the early days. Compliance tools built on simple rule-based systems using Boolean logic.
Speaker 1
Right. The classic, if this, then that. If X, A, and D, Y, then Z.
Speaker 2
Exactly.
Speaker 1
Yeah.
Speaker 2
That was key for things like advertising review, wasn't it?
Speaker 1
Yeah.
Speaker 2
Reducing errors, standardizing things.
Speaker 1
Oh, absolutely. Those systems were the bedrock. They let software think systematically, predictably.
Speaker 2
It was automation, really.
Speaker 1
Yeah.
Speaker 2
Laying the groundwork before the big AI explosion.
Speaker 1
Foundational stuff. Effective for its time, but not what we think of as AI now.
Speaker 2
Which brings us to the big shift. This is where it gets really interesting, I think. The arrival of generative AI.
Speaker 1
Ah, yes. The chat GPTs, the clods, the Geminis of the world.
Speaker 2
Right. Our source calls these types of large language models LLMs. They can understand text, generate human-like text, which is huge for compliance.
Speaker 1
Huge. It's like these models have read, well, practically everything. Legal codes, market data, everything.
 
And they can generate analysis, even draft documents.
Speaker 2
And firms are using techniques like prompt engineering to sort of guide the AI.
Speaker 1
Precisely. It's becoming a new skill, learning how to ask the AI the right question to get a useful, specific answer for a regulatory problem. Turning complex regs into something actionable.
Speaker 2
Okay. So how are these powerful, you know, uppercase AI tools actually being used in compliance right now? Like real-world examples.
Speaker 1
Good question. Well, imagine an LLM just tearing through thousands of pages of new regulations. Yeah.
 
Pulling out the key changes, maybe drafting summary alerts for the legal team. Stuff that used to take weeks.
Speaker 2
Okay. That's a time saver.
Speaker 1
Big time. So regulatory interpretation, document drafting, even catching errors before a human sees them. And there's this bring-your-own-model idea, too.
 
BYM.
Speaker 2
BYM.
Speaker 1
Yeah. Bring-your-own-model. Let's firms plug in their preferred LLM.
 
Gives them flexibility, control, especially with their own sensitive data.
Speaker 2
Makes sense. But, you know, with great power, there have got to be challenges, right? The glossary mentions black-box models.
Speaker 1
Uh-huh. That's a big one. AI systems where you can't easily see how they reached a decision.
Speaker 2
Which sounds, frankly, a bit scary for compliance, where you need audit trails and transparency.
Speaker 1
Exactly. It really highlights why we need explainable AI, or xAI. It's not just tech jargon.
 
It's about making sure we can actually understand and trust the AI's output. Why did it flag that transaction? You need to know.
Speaker 2
Okay. Explainability is key. What other hurdles are there?
Speaker 1
Well, another critical one is hallucinations.
Speaker 2
Hallucinations. Like the AI is making things up.
Speaker 1
Essentially, yes. Generating outputs that are just plain wrong or fabricated. In compliance, that's obviously a massive risk.
 
Imagine an AI drafting a completely inaccurate regulatory filing.
Speaker 2
Yikes. Nightmare fuel for a compliance officer. Right.
Speaker 1
That's why concepts like guardrails and responsible AI are absolutely essential.
Speaker 2
Guardrails meaning setting limits.
Speaker 1
Setting boundaries. Programmatic limits to stop the AI going off the rails, basically. Responsible AI is the whole framework, making sure it's ethical, fair, aligned with regulations.
Speaker 2
Got it. You mentioned the long game, too. What about model drift?
Speaker 1
Ah, yes. Model drift. This is where an AI model's accuracy can decay over time.
Speaker 2
How does that happen?
Speaker 1
Well, the real world changes, right? New data patterns emerge, regulations get updated. If the AI isn't kept up to date, it's understanding can drift and it starts making mistakes.
Speaker 2
So it's not a set it and forget it kind of technology.
Speaker 1
Definitely not. It needs ongoing monitoring, validation, retraining. It's a continuous process, a new kind of maintenance challenge for compliance teams.
Speaker 2
Okay, so wrapping this up, what's the big picture takeaway for you listening in? We've seen AI evolve in compliance from basic rules to these incredibly powerful generative AI and LLMs. Right.
Speaker 1
We've seen the practical uses, interpretation, drafting, but also flagged the big challenges. Black boxes, hallucinations, model drift.
Speaker 2
So question really becomes, as AI gets deeper into compliance work, how do professionals adapt? It's not just about using the tools, is it?
Speaker 1
No, it's also about critical oversight, making sure the outputs are accurate, fair, transparent, accountable, that human judgment is still vital.
Speaker 2
So maybe the final thought for you is, what part of this AI shift feels most significant for your role or for the future of compliance as you see it? What really stood out today?

AI GLOSSARY
AI TERMS FOR COMPLIANCE PROFESSIONALS

Artificial Intelligence (AI) has the promise of becoming a transformative force in the financial services sector, eventually reshaping how compliance professionals approach their work. At Red Oak, our journey with AI has been careful and pragmatic. From leveraging foundational rule-based systems to harnessing the power of LLMs, we've evolved our approach to ensure that the compliance processes we support are efficient, effective, scalable and secure. This glossary reflects the key terms we've encountered along the way and aims to empower compliance professionals with the knowledge to navigate this ever-evolving landscape.

From Rules-Based Systems to Generative AI

Initially, Red Oak’s compliance solutions were built on what we now call “lowercase ai.” This included rule-based systems and Boolean logic—fundamental components that allowed software to “think” systematically. These systems powered features like our lexicon and rules-based workflows in the Advertising Review module, providing a strong foundation for error reduction and process standardization.

The release of generative AI tools like ChatGPT, Claude, and Gemini marked a turning point. At Red Oak, we’ve embraced “uppercase AI,” utilizing advanced techniques like prompt engineering and large language models (LLMs) to create our AI Review module. This innovative addition to our Advertising Review platform helps compliance teams catch pre-review errors, streamlining the submission process and improving efficiency. By integrating these advancements, we’ve positioned ourselves at the forefront of AI-driven compliance solutions.

Below, we present a glossary of AI terms, carefully curated for compliance professionals in the financial services industry.

A
  • Anthropic (Claude): A company specializing in AI safety and research, offering models like Claude, which prioritize ethical and secure AI interactions. As one of the many Large Language Models (LLMs) available, firms can utilize Claude through Red Oak’s Bring
    Your Own Model (BYOM) approach and integrate it with the Red Oak AI Review module.
  • Artificial Intelligence (AI): The creation of systems that simulate human intelligence to perform tasks such as decision-making and pattern recognition.
  • Black Box Models: AI systems whose internal decision-making processes are not easily interpretable. For example, a model trained on a limited set of proprietary data might produce outputs that inadvertently reflect biases or fail to account for broader regulatory
    contexts, necessitating rigorous validation and oversight.
  • Boolean Logic: A system of binary operations (true/false, 1/0) used in rule-based systems to filter data or identify anomalies. Boolean logic underpins many foundational compliance tools.
  • Bring Your Own Model (BYOM): An approach allowing organizations to integrate their custom or third-party AI models into existing platforms. For example, Red Oak allows firms to use their preferred LLM—such as Anthropic’s Claude or OpenAI’s ChatGPT—and integrate it into the AI Review module.
  • Closed-Loop System: A system where the AI continuously monitors its own outputs and uses feedback from those outputs to adjust its behavior and improve its performance over time, essentially creating a self-regulating loop that learns from its results and adapts
    accordingly.
  • Continuous Learning Models: AI models that evolve over time by learning from new data. These are valuable for detecting novel compliance risks or fraud patterns.
  • Conversational AI: Systems designed to simulate human conversation, such as chatbots or AI interfaces, like ChatGPT.
  • Decision Trees: A machine learning method using branching structures to model decisions and outcomes. Compliance teams use decision trees for scenario analysis and rule-based decision-making.
  • Deep Learning: A subset of machine learning that utilizes multi-layered neural networks to analyze complex patterns. Applications include fraud detection and compliance monitoring.
  • Explainable AI (XAI)/Explainability: AI systems designed to produce interpretable outputs.
  • Foundational Model: Large pretrained models, such as GPT, serving as the basis for
    task-specific applications. These models streamline regulatory text interpretation and
    compliance workflows.
  • Generative AI (GenAI): AI capable of creating content like text or images. Current LLMs like ChatGPT, Claude, and Gemini are types of GenAI platforms.
  • Generative Pretrained Transformer (GPT): A type of generative AI model that uses deep learning to produce human-like text based on given prompts. In compliance, GPT models, such as OpenAI’s ChatGPT, help streamline processes like regulatory text interpretation, document drafting, and identifying potential risks in advertising materials.
  • Generalized Model: The ability of an AI model to apply learned knowledge to new, unseen data.
  • Google (Gemini): A suite of advanced AI technologies developed by Google, including tools and models designed to enhance data analysis, compliance workflows, and operational efficiency in regulated industries like financial services. As one of the many Large Language Models (LLMs) available, firms can utilize Gemini through Red Oak’s Bring Your Own Model (BYOM) approach and integrate it with the Red Oak AI Review module.
  • Guardrails: Mechanisms within AI systems to ensure outputs meet ethical, legal, and regulatory standards. These are essential for maintaining trust and compliance integrity.
  • Hallucinations: Erroneous outputs generated by AI models. Minimizing hallucinations is critical in compliance to avoid inaccurate or misleading interpretations.
  • Hybrid AI: Combines traditional rule-based systems with machine learning techniques. In compliance, hybrid AI integrates established regulatory frameworks with advanced analytics.
  • Image Recognition: AI’s ability to analyze visual elements. Applications in compliance include verifying document authenticity or monitoring physical assets.
  • Large Language Models (LLMs): Extensive AI models trained on large datasets to understand and generate text. ChatGPT, Claude, and Gemini are considered LLMs.
  • Machine Learning (ML): A branch of AI where systems improve performance by learning from data. Compliance teams might use ML for predictive modeling and anomaly detection.
  • Model: The mathematical framework underpinning AI systems. Compliance models focus on tasks like fraud detection, risk assessment, and transaction monitoring.
  • Model Drift: A decline in model accuracy over time due to changing data patterns. Identifying and mitigating drift ensures compliance models remain effective.
  • Natural Language Generation (NLG): AI’s capability to create coherent and meaningful text. Compliance teams may use NLG for drafting regulatory reports and client communications.
  • Natural Language Processing (NLP): AI’s ability to analyze and interpret human language. NLP assists compliance tools by extracting key insights from regulatory documents.
  • Natural Language Understanding (NLU): A subset of NLP focused on comprehending text meaning. In compliance, NLU aids in interpreting complex regulations.
  • Neural Networks: AI systems modeled after the human brain, designed for complex pattern recognition. These are applied in fraud detection and anomaly analysis.
  • OpenAI (ChatGPT): An AI platform offering conversational and generative capabilities. Red Oak’s integration of ChatGPT enhances compliance reviews by identifying pre-review issues.
  • Pretrained Model: An AI model trained on general data before being fine-tuned for specific applications. Compliance professionals may use pretrained models for efficient adaptation to regulatory needs.
  • Private AI: AI systems deployed in secure environments to ensure data confidentiality. In compliance, private AI may be employed to protect sensitive information.
  • Prompt: Input provided to an AI model to elicit a response. In compliance, prompts help tailor outputs to specific regulatory questions.
  • Prompt Engineering: The design of effective prompts to optimize AI outputs. Red Oak’s compliance tools use prompt engineering to align AI with firm-specific policies.
  • Responsible AI: AI developed with attention to fairness, transparency, and
    accountability. In compliance, responsible AI ensures ethical and regulatory alignment.
  • Small Language Models (SLMs): Compact AI models optimized for specific tasks. These models can be effective in analyzing financial documents for compliance purposes.
  • Supervised Learning: Machine learning where models train on labeled data. In compliance, this method identifies known patterns of fraud or non-compliance.
  • Tokenization: The process of breaking text into smaller units (tokens) for AI processing. Tokenization can help when analyzing complex regulatory texts.
  • Tokens: Basic data units used in AI models. In compliance, tokens enable precise text analysis.
  • Training Data/Training Set: The dataset used to teach AI models. Compliance applications often utilize historical transaction records or regulatory examples.
  • Unsupervised Learning: Machine learning where models find patterns in unlabeled data. In compliance, this may identify new risks or anomalies.
  • Virtual Agents: AI-powered systems designed to interact with users. In compliance, virtual agents can provide guidance on regulatory processes and answer queries.

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