Amazon SageMaker JumpStart Industry: Financial

Use SageMaker JumpStart Industry: Financial solutions, models, and example notebooks to learn about SageMaker features and capabilities through curated one-step solutions and example notebooks of industry-focused machine learning (ML) problems. The notebooks also walk through how to use the SageMaker JumpStart Industry Python SDK to enhance industry text data and fine-tune pretrained models.

Amazon SageMaker JumpStart Industry Python SDK

SageMaker Runtime JumpStart provides processing tools for curating industry datasets and fine-tuning pretrained models through its client library called SageMaker JumpStart Industry Python SDK. For detailed API documentation of the SDK, and to learn more about processing and enhancing industry text datasets for improving the performance of state-of-the-art models on SageMaker JumpStart, see the SageMaker JumpStart Industry Python SDK open source documentation.

Amazon SageMaker JumpStart Industry: Financial Solution

SageMaker JumpStart Industry: Financial provides the following solution notebooks:

• Corporate Credit Rating Prediction

This SageMaker JumpStart Industry: Financial solution provides a template for a text-enhanced corporate credit rating model. It shows how to take a model based on numeric features (in this case, Altman's famous 5 financial ratios) combined with texts from SEC filings to achieve an improvement in the prediction of credit ratings. In addition to the 5 Altman ratios, you can add more variables as needed or set custom variables. This solution notebook shows how SageMaker JumpStart Industry Python SDK helps process Natural Language Processing (NLP) scoring of texts from SEC filings. Furthermore, the solution demonstrates how to train a model using the enhanced dataset to achieve a best-in-class model, deploy the model to a SageMaker endpoint for production, and receive improved predictions in real time.

• Graph-Based Credit Scoring

Credit ratings are traditionally generated using models that use financial statement data and market data, which is tabular only (numeric and categorical). This solution constructs a network of firms using SEC filingsand shows how to use the network of firm relationships with tabular data to generate accurate rating predictions. This solution demonstrates a methodology to use data on firm linkages to extend the traditionally tabular-based credit scoring models, which have been used by the ratings industry for decades, to the class of machine learning models on networks.

Note

The solution notebooks are for demonstration purposes only. They should not be relied on as financial or investment advice.

You can find these financial services solutions through the SageMaker JumpStart page in Studio Classic.

Important

As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see Amazon SageMaker Studio.

Note

The SageMaker JumpStart Industry: Financial solutions, model cards, and example notebooks are hosted and runnable only through SageMaker Studio Classic. Log in to the SageMaker console, and launch SageMaker Studio Classic. For more information about how to find the solution card, see the previous topic at SageMaker JumpStart.

Amazon SageMaker JumpStart Industry: Financial Models

SageMaker JumpStart Industry: Financial provides the following pretrained Robustly Optimized BERT approach (RoBERTa) models:

• Financial Text Embedding (RoBERTa-SEC-Base)

• RoBERTa-SEC-WIKI-Base

• RoBERTa-SEC-Large

• RoBERTa-SEC-WIKI-Large

The RoBERTa-SEC-Base and RoBERTa-SEC-Large models are the text embedding models based on GluonNLP's RoBERTa model and pretrained on S&P 500 SEC 10-K/10-Q reports of the decade of the 2010's (from 2010 to 2019). In addition to these, SageMaker JumpStart Industry: Financial provides two more RoBERTa variations, RoBERTa-SEC-WIKI-Base and RoBERTa-SEC-WIKI-Large, which are pretrained on the SEC filings and common texts of Wikipedia.

You can find these models in SageMaker JumpStart by navigating to the Text Models node, choosing Explore All Text Models, and then filtering for the ML Task Text Embedding. You can access any corresponding notebooks after selecting the model of your choice. The paired notebooks will walk you through how the pretrained models can be fine-tuned for specific classification tasks on multimodal datasets, which are enhanced by the SageMaker JumpStart Industry Python SDK.

Note

The model notebooks are for demonstration purposes only. They should not be relied on as financial or investment advice.

The following screenshot shows the pretrained model cards provided through the SageMaker JumpStart page on Studio Classic.

Note

The SageMaker JumpStart Industry: Financial solutions, model cards, and example notebooks are hosted and runnable only through SageMaker Studio Classic. Log in to the SageMaker console, and launch SageMaker Studio Classic. For more information about how to find the model cards, see the previous topic at SageMaker JumpStart.

Amazon SageMaker JumpStart Industry: Financial Example Notebooks

SageMaker JumpStart Industry: Financial provides the following example notebooks to demonstrate solutions to industry-focused ML problems:

• Financial TabText Data Construction – This example introduces how to use the SageMaker JumpStart Industry Python SDK for processing the SEC filings, such as text summarization and scoring texts based on NLP score types and their corresponding word lists. To preview the content of this notebook, see Simple Construction of a Multimodal Dataset from SEC Filings and NLP Scores.

• Multimodal ML on TabText Data – This example shows how to merge different types of datasets into a single dataframe called TabText and perform multimodal ML. To preview the content of this notebook, see Machine Learning on a TabText Dataframe – An Example Based on the Paycheck Protection Program.

• Multi-category ML on SEC filings data – This example shows how to train an AutoGluon NLP model over the multimodal (TabText) datasets curated from SEC filings for a multiclass classification task. Classify SEC 10K/Q Filings to Industry Codes Based on the MDNA Text Column.

Note

The example notebooks are for demonstrative purposes only. They should not be relied on as financial or investment advice.

Note

The SageMaker JumpStart Industry: Financial solutions, model cards, and example notebooks are hosted and runnable only through SageMaker Studio Classic. Log in to the SageMaker console, and launch SageMaker Studio Classic. For more information about how to find the example notebooks, see the previous topic at SageMaker JumpStart.

To preview the content of the example notebooks, see Tutorials – Finance in the SageMaker JumpStart Industry Python SDK documentation.

Amazon SageMaker JumpStart Industry: Financial Blog Posts

For thorough applications of using SageMaker JumpStart Industry: Financial solutions, models, examples, and the SDK, see the following blog posts:

• Use pre-trained financial language models for transfer learning in Amazon SageMaker JumpStart

• Use SEC text for ratings classification using multimodal ML in Amazon SageMaker JumpStart

• Create a dashboard with SEC text for financial NLP in Amazon SageMaker JumpStart

• Build a corporate credit ratings classifier using graph machine learning in Amazon SageMaker JumpStart

• Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

Amazon SageMaker JumpStart Industry: Financial Related Research

For research related to SageMaker JumpStart Industry: Financial solutions, see the following papers:

• Context, Language Modeling, and Multimodal Data in Finance

• Multimodal Machine Learning for Credit Modeling

• On the Lack of Robust Interpretability of Neural Text Classifiers

• FinLex: An Effective Use of Word Embeddings for Financial Lexicon Generation

Amazon SageMaker JumpStart Industry: Financial Additional Resources

For additional documentation and tutorials, see the following resources:

• The SageMaker JumpStart Industry: Financial Python SDK

• SageMaker JumpStart Industry: Financial Python SDK Tutorials

• The SageMaker JumpStart Industry: Financial GitHub repository

• Getting started with Amazon SageMaker - Machine Learning Tutorials