The finance and banking industry can significantly enhance investment research by integrating generative AI into daily operations such as financial statement analysis. By leveraging advanced natural language processing (NLP) capabilities and data analysis techniques, you can streamline common tasks in the financial industry, including:
- Automate data extraction – Manual data extraction processes for analyzing financial statements can be time-consuming and prone to human error. Generative AI models can automate the search and extraction of financial data from documents such as 10-Ks, balance sheets, and income statements. The Fundamental Model (FM) is trained to identify and extract relevant information such as expenses, revenue, and debt.
- Trend analysis and forecasting – Identifying and predicting trends requires domain expertise and advanced mathematics. This limits the ability of individuals to run one-time reports and creates dependencies on a small number of employees within the organization. Generative AI applications can analyze financial data and identify trends and patterns while predicting future financial performance without any manual intervention by analysts. By removing manual analysis steps and allowing generative AI models to create reports that analyze trends in financial statements, organizations can become more agile and make faster decisions in the marketplace.
- financial report – Manually creating detailed financial analysis reports can be time and resource intensive. Dedicated resources to generate financial statements can create bottlenecks within an organization, requiring specialized roles to transform financial data into consumable stories. FMs can summarize financial statements, highlight key metrics found through trend analysis, and provide insight. Automated reporting processes not only provide consistency and speed, but also minimize resource constraints on the financial reporting process.
Amazon Bedrock is a fully managed service that makes leading AI startups and Amazon’s FM available through an API. So you can choose from a wide range of FMs to find the best model for your use case. Amazon Bedrock provides a serverless experience, so you can get started quickly, privately customize FM with your own data, and quickly integrate it into your applications using AWS tools, without managing any infrastructure. can be deployed.
This post shows you how to deploy generative AI applications that can accelerate financial statement analysis on AWS.
Solution overview
Building a generative AI application that analyzes financial statements using Amazon Bedrock requires a series of steps, from setting up the environment to deploying the model and integrating it into your application.
The following diagram shows an example solution architecture using AWS services.
The workflow consists of the following steps:
- Users interact with a web or mobile application to upload financial documents.
- Amazon API Gateway manages and routes incoming requests from your UI.
- An AWS Lambda function is invoked when a new document is added to an Amazon Simple Storage Service (Amazon S3) bucket.
- Amazon Bedrock analyzes documents stored in Amazon S3. The analysis results are sent back to your S3 bucket through a Lambda function and stored there.
- Amazon DynamoDB provides a fast and scalable way to store, retrieve, and display metadata and analysis results to your users.
- Amazon Simple Notice Service (Amazon SNS) sends notifications to application users about the status of document processing.
The following sections describe important considerations for each step of building and deploying a generative AI application.
Prepare the data
Collect the financial statements you want to analyze. These include the balance sheet, income statement, cash flow statement, etc. Make sure your data is clean and consistently formatted. In some cases, you may need to preprocess your data to remove noise and standardize the format. Preprocessing data transforms raw data into a state that can be used efficiently to train models. This is necessary because real-world data is often confusing and inconsistent. The result is consistent data for the model to ingest. The two most common types of data preprocessing are normalization and standardization.
Normalization involves changing the numeric columns in your dataset to standardize their scale. By rearranging the data in the dataset, scaling techniques reduce duplication where numbers are scaled from 0 to 1. Because outliers are removed, undesirable characteristics are also removed from the dataset. When working with large amounts of data, normalizing the dataset improves the performance of machine learning models in environments where the distribution of features is unclear.
Standardization is a method designed to rescale the values in a dataset so that they meet the properties of the standard normal distribution. This method allows data to be sent more reliably between systems, making it easier to process, analyze, and store data in databases. Standardization is useful when the distribution of features is consistent and the values on the scale are not constrained within a certain range.
Please select a model
Amazon Bedrock gives you choice by providing a flexible and scalable environment where you can access and use multiple FMs from leading AI model providers. This flexibility allows you to choose the most appropriate model for your specific use case, whether you’re working on tasks like NLP, text generation, image generation, or other AI-driven applications.
Deploy the model
If you do not yet have access to Amazon Bedrock FM, you must request access through the Amazon Bedrock console. You can then deploy the selected model using the Amazon Bedrock console. Configure the deployment settings according to your application requirements.
Develop a backend application
Create a backend service to interact with the deployed model. This service handles requests from the front end, sends data to the model, and processes the model’s response. You can use Lambda, API Gateway, or any other preferred REST API endpoint.
Use the Amazon Bedrock API to send financial statements to your model and receive analysis results.
Below is an example backend code.
Develop front-end UI
Create a front-end interface for users to upload financial statements and view analysis results. This can be a web application or a mobile application. Make sure your frontend can send financial statement data to your backend service and display the analysis results.
conclusion
In this post, we discussed the benefits of leveraging Amazon Bedrock to build generative AI applications that accelerate the analysis of financial documents. Stakeholders can now deploy and manage LLM using AWS services, helping improve efficiency in extracting insights from common documents such as 10-Ks, balance sheets, and income statements.
To learn more about working with generative AI on AWS, see the AWS Skill Builder’s generative AI training module.
For instructions on building front-end and full-stack applications with Amazon Bedrock, see Front-End Web and Mobile on AWS and Building a Full-Stack, Sample Web App with Amazon Bedrock.
About the author
Jason Dalba is a leading AWS solution architect focused on enterprise applications, helping customers design highly available and scalable data and AI solutions.
