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The process of creating loan agreements is a critical function for financial institutions (FIs) such as credit unions, commercial banks, and brokerage firms. One essential tool in this process is the term sheet, a non-binding document that outlines the key terms and conditions of a prospective loan. However, drafting term sheets can be a complex and time-consuming process, often involving the extraction and interpretation of data from various sources such as call transcripts, company documents, and financial statements.

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AI21’s Generative AI models are built to automate and streamline this process. Specifically, by leveraging the power of AI21's Contextual Answers and Jamba-Instruct Foundation Model, FIs can now generate term sheets more efficiently and accurately than ever before. In this post, we will cover this solution’s architecture and how it’s designed to optimize for accuracy and precision when generating the final term sheet.

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You can find code showing a step-by-step walkthrough of this approach here.

Architectural approach

The solution leverages a two-step process:

1. Information extraction: AI21's Contextual Answers model is employed to sift through and extract relevant information from various sources, such as call transcripts and company documents. This model is designed to understand the context and nuances of the input data, allowing it to identify and extract the most pertinent details related to the potential loan.
   
   
2. Term sheet generation: The extracted information is then fed into a chain of Jamba-Instruct Foundation Model calls. This first drafts an initial term sheet, ensuring that it includes all necessary sections and clauses. It then critiques the draft for any missing elements, making adjustments as necessary.
   
   
3. Final term sheet: The final phase of this process is generating the enhanced term sheet. Jamba-Instruct synthesizes the feedback and the original draft to create a final version of the term sheet. This document is a comprehensive refinement that incorporates the insights and suggestions generated during the critical analysis phase. The result is a term sheet that is not only accurate and complete but also can be tailored to the specific needs and standards of the financial institution.

The approach is schematically shown below:

The final output includes not only a term sheet that has high fidelity to the original data (e.g. loan amount) but also highlights any key terms that may be missing (e.g. disclaimers). Finally, Jamba-Instruct can then create new terms for these missing sections based on the context, saving valuable time for human reviewers. This not only saves time and resources but also minimizes the risk of omissions or errors that could lead to legal or financial issues later on.

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A key point in this workflow is also the use of Contextual Answers, which demonstrates the essential lesson that different tools are suited to different use cases. In the realm of finance, where the stakes are high and the terminology complex, employing a tool specifically designed for financial document generation is not just beneficial—it's essential. This approach not only streamlines the term sheet generation process but also enhances the reliability and accuracy of the financial agreements that underpin investments and loans.

Getting started with term sheet automation

In conclusion, the integration of AI21's Generative AI models (Jamba-Instruct and Contextual Answers) into the term sheet generation process is a testament to the power of Task-Specific Models and general purpose LLMs working jointly together—just one example of the robust AI systems AI21 builds. By leveraging these models, financial institutions can ensure that their term sheets are not only generated quickly but are also accurate, comprehensive, and tailored to their specific needs. This approach not only streamlines the term sheet generation process but also underscores the critical role of using the right tools for different tasks, a lesson that is essential for success in the complex world of finance.

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Jamba-Instruct is now available in public preview. Start building today