Building a GPT for a bank’s compliance activities. Is it possible?

API
, , ,
22

Thank you! Would this be created via a vector database, or are they any low code/no code options that exist today that can allow me to build a foundation model via a UI?

23

You would use a nosql solution put the regulation in a document database. Whatever option you choose. Microsoft, aws, or Google are some. Ibm. Has some good ones. Check the pricing. One thing in azure cost $24,000 a month. Google has a conversational service that’s $1000 amonth. As an example.

24

Here’s a basic outline for azure ai.

(Attachment Compliance_Analysis_Integration_Document (1).docx is missing)

25

Thanks for the info – much appreciated!

26

Would you be able to DM me the document. The post says:

(Attachment Compliance_Analysis_Integration_Document (1).docx is missing)

27

I’ll email you outlines of different platforms later today.
If that would be helpful to you.

28

That would be extremely helpful – thank you!

29

This is just an out line for the azure ai cloud. Here is the document.

Integration of Foundation Models in Compliance Analysis for Banking Sector

  1. Choose the Right Foundation Model:
  • Select a model like GPT or another advanced NLP model suitable for understanding legal and financial language.
  1. Fine-Tuning for Specific Tasks:
  • Fine-tune the foundation model on banking regulations, internal policies, and legal texts to enhance its understanding of the domain.
  1. Data Preparation:
  • Prepare and annotate a dataset comprising the bank’s internal documents and relevant legal texts.
  1. Integration with Azure Services:
  • Use Azure Cognitive Search for data indexing and retrieval.
  • Leverage Azure Machine Learning for training and deploying the model.
  1. Building Compliance Analysis Features:
  • Develop features for document comparison, compliance issue identification, and regulation alignment.
  1. Application Development:
  • Build an application for the model’s use, considering low-code solutions for interfaces and integrations.
  1. Security and Compliance Considerations:
  • Ensure data privacy and model security, especially with sensitive financial information.
  1. Continuous Learning and Updating:
  • Implement a system for ongoing learning from new data and adapting to legal and policy changes.
  1. Human-in-the-Loop:
  • Integrate human oversight for validation and accuracy assurance.
  1. Testing and Deployment:
  • Conduct thorough real-world scenario testing and plan a phased deployment.
  1. Maintenance and Improvement:
  • Regularly evaluate and update the model based on performance data and user feedback.
2 Likes
30

Here is how you would do this in google cloud using their services.

Here are the general steps you would need to follow to build such a solution using Google Cloud AI services:

  1. Data Collection and Preprocessing:

    1. Gather all the in-house documents, policies, procedures, and controls from the bank.
    2. Collect relevant laws and regulations that apply to the banking industry.
    3. Preprocess the text data, including cleaning, tokenization, and structuring the data.

  2. Custom NLP Model Development:

    1. You would typically need to build a custom NLP model, such as a fine-tuned GPT-based model, to understand and analyze the text content effectively.
    2. You can use Google Cloud’s AutoML Natural Language or AI Platform for model training if you have labeled data. Alternatively, you can use pre-trained language models like BERT or GPT-3 and fine-tune them with your specific data.

  3. Knowledge Graph:

    1. Create a knowledge graph that links specific processes, policies, and procedures to the applicable laws and regulations. This graph will serve as a reference for your model to make connections.

  4. Development and Integration:

    1. Develop an application or system that allows users to input in-house documents.
    2. The system should use the custom NLP model to analyze the documents, extract relevant information, and compare it to the knowledge graph to identify any non-compliance issues.

  5. Feedback Loop:

    1. Implement a feedback loop to continuously improve the model’s accuracy and performance based on user feedback and evolving regulations.

  6. Deployment:

    1. Deploy your solution on Google Cloud infrastructure, such as Google Kubernetes Engine (GKE) or AI Platform, for scalability and reliability.

  7. Monitoring and Compliance Reporting:

    1. Implement monitoring and reporting capabilities to track compliance and generate reports for the bank.

While Google Cloud provides various AI and ML services that can be used in different parts of this solution, building such a complex system would likely require the involvement of data scientists, NLP experts, and software developers. It may not be feasible to create a fully functional solution using a no-code/low-code approach, given the intricacies of this use case.

2 Likes