(n) information that does not have a predefined format or organized structure, making it more complex and challenging to analyze and process compared to structured data.

Ex., a borrower submits a PDF that includes text, pictures of their product, and a screenshot of their financials isntead of a table. All this information is valuable, but since it’s not standardized in a tabular format the underwriter spends time manually copying and pasting it into a credit workbook. Compare that to structured data, which resides in relational databases and is easily searchable (e.g., rows and columns of a spreadsheet) and fits neatly into a tabular schema.

Examples of Unstructured Data:

- Text documents (e.g., PDFs, Word files).

- Multimedia files (e.g., images, videos, audio recordings).

- Social media content (e.g., posts, tweets, comments).

- Email communications.

- Web pages.

- Logs and machine-generated data (e.g., sensor data, IoT device streams).

- Customer feedback and reviews.

- Call transcripts or customer service recordings.

Work with Unstructured Data in Automated Underwriting

Unstructured data unlocks the deeper insights needed for better decision making. We built our new document uploader to support data ingestion from any file type used to fuel credit decisions so that credit teams can efficiently take into account these insights alongside structured data more easily parsed.

Our team aimed to create an AI credit underwriting tool to address unstructured data’s:

1. Searchability: Makes even predefined fields easy to query.

2. Analysis: Process and analyze any media type.

3. Integration: Seamlessly combine with structured data.