Customer financial profiler

[sharathrushi]

Given a customer and his financial documents
This notebook does the following:

1. Checks whether the customer is involved in money laundering or illegal activities on google search
2. Retrieves customer's monthly income, total assets amount.

This is helpful for financial institutions use cases for loan eligibility.

Colab link

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB

[gemini-code-assist]

Summary of Changes

Hello @sharathrushi, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a practical example showcasing the application of the Gemini API for customer financial profiling. The new notebook provides a framework for financial institutions to assess loan eligibility by analyzing customer financial documents and identifying potential risks, thereby streamlining the evaluation process.

Highlights

• New Example Notebook: A new example notebook titled 'Customer Financial Profiler' has been added to the examples directory. This notebook demonstrates how to use the Gemini API for financial profiling.
• README Update: The examples/README.md file has been updated to include an entry for the new 'Customer Financial Profiler' notebook, providing its description, category, and a direct Colab link.

Changelog

• examples/README.md
  • Added a new entry to the examples table for the 'Customer Financial Profiler' notebook.
  • Included a description highlighting its utility for estimating financial status and assessing loan eligibility.
  • Categorized the example under 'Gemini API and its models and features'.
  • Provided a direct Colab link for easy access and experimentation.

Activity

• No human activity (comments, reviews) has been recorded on this pull request yet.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces a new notebook, 'Customer Financial Profiler', which demonstrates how to use Gemini to analyze financial documents. The notebook is well-structured and provides a useful example for financial use cases.

My review focuses on improving adherence to the repository's style guide, enhancing code quality, and addressing a critical issue with data sourcing. Key points include:

• Correcting the use of a personal GCS bucket for sample files.
• Improving code readability and maintainability by removing unused imports and simplifying logic.
• Aligning with notebook formatting standards, such as using sentence case for titles and resetting execution counts.
• Making the notebook more robust by avoiding hardcoded values for text truncation.

I've also included a minor formatting suggestion for the README.md update. Overall, this is a great contribution, and with these changes, it will be an excellent addition to the cookbook.

[sharathrushi]

@Giom-V , requesting your comment on this.
Awaiting for any suggestions or changes, I will surely be incorporating them