Using LangGraph to Read Regulatory Guidelines(Python Code included)

AI Use Cases in Banking

Problem Statement

In the banking and financial services industry, reading and analyzing dense regulatory guidelines is a frequent ask, whether it's the OCC’s latest advisory, a new RBI circular, or updates from the FCA. Usually, such an exercise is undertaken to answer the following questions:

• What are the key compliance obligations?

• Are there any changes from previous circulars?

• What are the exceptions or edge-case clauses?

• What timelines or thresholds are being introduced?

• What are the roles/responsibilities of the different stakeholders involved?

• How would the changes proposed impact the current operations?

Analyses such as these are used for senior management reporting, updating existing policies, and modifying operational processes.

Reading and interpreting these guidelines is tedious as these regulatory texts are often lengthy, ambiguous, and full of edge cases that demand context-aware interpretation. In many cases, cross-referencing may also be required to other documents such as internal policies and other guidelines, which makes the job even more laborious, time-consuming, and error-prone. Thankfully, AI can ease this task significantly.

In this article, we look at how tools such as Langraph can be used to extract the right information from lengthy regulatory guidelines.

To explain the case further, I have attempted to build an illustrative chatbot on top of a regulatory guideline.

The Solution: LangGraph-Powered Reader

LangGraph is an open-source framework that extends LangChain's capabilities by allowing developers to define stateful, multi-agent, or step-based workflows using a graph structure. Unlike simple chains or reactive agents, LangGraph workflows can revisit prior steps, branch based on conditions, and maintain memory across multiple decision points.

In short, it's perfect for structured reading, summarization, and interpretation of regulatory documents.

LangGraph allows us to design a reusable agent that:

1. Reads the document section by section.

2. Summarizes key obligations and exceptions.

3. Cross-references earlier versions or related circulars.

4. Flags potential action points or compliance deadlines.

5. Escalates ambiguities to a human reviewer or domain expert.

My Experiment

I built a basic chatbot that answers questions based on the Monetary Authority of Singapore Guidelines on Outsourcing (Banks) using LangGraph, LangChain, and OpenAI’s GPT-4.

Tools Used

• LangChain: Framework for working with LLMs and documents

• LangGraph: Orchestration layer for creating modular, state-based flows

• FAISS: Vector store to search embedded document chunks

• OpenAI GPT-4: To generate responses from the retrieved context

• Google Colab: Hosted environment to avoid local memory issues

What the Chatbot Does

1. Loads and chunks a regulatory PDF

2. Converts chunks into embeddings

3. Retrieves relevant text using semantic similarity

4. Uses GPT-4 to answer user queries from the document

Git Hub

You can find the code to execute the Chatbot here (Pls click on this link)

or copy + paste this: https://github.com/kasrangan/Portfolio/blob/main/MAS_Chatbot_LangGraph_Colab.ipynb

Make sure the following dependencies are installed/available:

• Jupyter Notebook installed

• Python 3.10+

• Access keys to OpenAI API (for GPT-4 or GPT-4o)

• In case laptop resources are inadequate, you can run the code on Google Colab

Benefits

• Domain-specific answers: No hallucinations — responses are grounded in a regulatory source

• Reusability: Works with any PDF document

• Modularity: LangGraph allows easy addition of new states, like escalation, logging, or audit trails

Limitations

• No fallback by default: If the answer is not present in the document, we get a "not found" message

• Initial setup is time-consuming: Type errors and LangGraph quirks require patience

• Context length limits: Long documents might still need smart chunking or summarization

Other Practical Use Cases

• RegTech Products: Build explainable compliance features for RegTech Products

• Internal Compliance Teams: Accelerate first-level review of new regulatory releases.

• Audit & Legal Functions: Reduce manual effort in interpreting overlapping regulations

Closing Thoughts

Transforming static regulatory documents into interactive tools can dramatically improve how compliance teams, product managers, and auditors access and apply critical information. By combining document parsing, semantic search, and large language models, it’s now possible to build chatbots that respond contextually and accurately, grounded entirely in regulatory text.

This approach not only reduces manual effort and ambiguity but also opens up possibilities for smarter compliance workflows, faster onboarding, and greater confidence in decision-making. While challenges around context limitations and fallback logic remain, the benefits of enabling natural language access to complex policies far outweigh the hurdles.

As regulations grow more intricate and dynamic, tools like this move us one step closer to intelligent, real-time compliance support, where anyone can ask a question and get an answer that’s both trustworthy and immediate.

This is just the beginning. I intend to add more complicated use cases to prove the concept. Watch this space.